MCP com FastMCP (1/4): crie seu primeiro servidor e gerencie suas tools

MCP com FastMCP (1/4): crie seu primeiro servidor e gerencie suas tools

Aviso: Este post foi traduzido para o português usando um modelo de tradução automática. Por favor, me avise se encontrar algum erro.

📚 **Esta entrada faz parte da série _MCP com FastMCP_**, dividida em quatro capítulos que se leem em ordem:

> * 👉 **Parte 1: Primeiro servidor e tools**

* Parte 2: Transporte, contexto y resources

* Parte 3: Recursos avançados e prompts

* Parte 4: HTTP, autenticação y cliente

O que é MCP?link image 53

MCP (Model Context Protocol) é um padrão open source desenvolvido pela Anthropic para permitir que os modelos de IA interajam com ferramentas externas por meio de um padrão

Até o desenvolvimento do protocolo MCP, quando queríamos que um LLM interagisse com ferramentas, tínhamos que criar código para poder interagir com a ferramenta e, por meio de function calling, enviar a informação ao LLM.

MCP vs API

Assim, por meio do MCP, um LLM pode interagir com ferramentas graças a um padrão. Dessa forma, se uma pessoa cria um servidor MCP, esse servidor pode ser reutilizado por outros com um único cliente. Se na tua aplicação desenvolves um cliente, podes descarregar um servidor MCP desenvolvido por outro e usá-lo sem problema.

Comumente, o MCP se assemelha ao padrão USB. Antes do USB, cada periférico tinha um tipo de conexão diferente: alguns tinham portas seriais, outros paralelas. Diferentes formatos de conectores, etc.

USB MCP

Com a chegada do USB, todos os periféricos se adaptaram a esse padrão, de modo que, com um único conector USB no teu computador, podes conectar quase qualquer periférico.

O MCP tem 7 componentes principais:

  • Host: Aplicação LLM que tem acesso a ferramentas MCP.
  • Servidor MCP: Servidor que realiza a comunicação com a API ou ferramenta que queremos expor ao LLM
  • Cliente MCP: Cliente que se conecta ao servidor MCP e realiza as solicitações
  • Ferramenta: Função que é executada no servidor MCP e que pode ser invocada pelo LLM
  • Recurso: Recurso que pode ser usado no servidor MCP. Geralmente concedem ao LLM acesso a recursos estáticos, como arquivos, bancos de dados, etc.
  • Resource template: Template para criar recursos dinâmicos. Através desses modelos, o LLM pode criar dinamicamente o recurso ao qual deseja acessar
  • Prompt: Prompt usado para gerar um prompt que será usado pelo LLM para interagir com o servidor MCP.

Um único host (aplicação) pode ter vários clientes. Cada cliente se conectará a um servidor MCP

mcp architecture

FastMCPlink image 54

Embora na documentação do MCP recomendem instalar mcp["cli"], há uma biblioteca criada por cima chamada fastmcp, que ajuda muito na hora de criar servidores MCP, então vamos usá-la

Criar ambiente virtuallink image 55

Para criar um servidor e um cliente MCP, vamos criar ambientes virtuais com uv com as dependências de que vamos precisar

Servidor MCPlink image 56

Primeiro, criamos uma pasta para o servidor MCP

	
< > Input
Python
!mkdir gitHub_MCP_server
Copied

Iniciamos o ambiente uv

	
< > Input
Python
!cd gitHub_MCP_server && uv init .
Copied
>_ Output
			
Initialized project `github-mcp-server` at `/Users/macm1/Documents/web/portafolio/posts/gitHub_MCP_server`

Ativamos isso

	
< > Input
Python
!cd gitHub_MCP_server && uv venv
Copied
>_ Output
			
Using CPython 3.11.11
Creating virtual environment at: .venv
Activate with: source .venv/bin/activate

E instalamos as bibliotecas necessárias

	
< > Input
Python
!cd gitHub_MCP_server && uv add anthropic fastmcp python-dotenv requests
Copied
>_ Output
			
Resolved 42 packages in 34ms
Installed 40 packages in 71ms
+ annotated-types==0.7.0
+ anyio==4.9.0
+ authlib==1.6.0
+ certifi==2025.6.15
+ cffi==1.17.1
+ charset-normalizer==3.4.2
+ click==8.2.1
+ cryptography==45.0.4
+ distro==1.9.0
+ exceptiongroup==1.3.0
+ fastmcp==2.9.0
+ h11==0.16.0
+ httpcore==1.0.9
+ httpx==0.28.1
+ httpx-sse==0.4.0
+ idna==3.10
+ jiter==0.10.0
+ markdown-it-py==3.0.0
+ mcp==1.9.4
+ mdurl==0.1.2
+ openapi-pydantic==0.5.1
+ pycparser==2.22
+ pydantic==2.11.7
+ pydantic-core==2.33.2
+ pydantic-settings==2.10.0
+ pygments==2.19.2
+ python-dotenv==1.1.1
+ python-multipart==0.0.20
+ requests==2.32.4
+ rich==14.0.0
+ shellingham==1.5.4
+ sniffio==1.3.1
+ sse-starlette==2.3.6
+ starlette==0.47.1
+ typer==0.16.0
+ typing-extensions==4.14.0
+ typing-inspection==0.4.1
+ typing-inspection==0.4.1

Cliente MCPlink image 57

Agora criamos uma pasta onde programaremos o cliente MCP

	
< > Input
Python
!mkdir client_MCP
Copied

Iniciamos o ambiente uv

	
< > Input
Python
!cd client_MCP && uv init .
Copied
>_ Output
			
Initialized project `client-mcp` at `/Users/macm1/Documents/web/portafolio/posts/client_MCP`

Nós ativamos isso

	
< > Input
Python
!cd client_MCP && uv venv
Copied
>_ Output
			
Using CPython 3.11.11
Creating virtual environment at: .venv
Activate with: source .venv/bin/activate

E, por último, instalamos as bibliotecas necessárias para o cliente.

	
< > Input
Python
!cd client_MCP && uv add anthropic fastmcp python-dotenv requests
Copied
>_ Output
			
Resolved 42 packages in 307ms
Prepared 5 packages in 115ms
Installed 40 packages in 117ms
+ annotated-types==0.7.0
+ anthropic==0.55.0
+ anyio==4.9.0
+ authlib==1.6.0
+ certifi==2025.6.15
+ cffi==1.17.1
+ charset-normalizer==3.4.2
+ click==8.2.1
+ cryptography==45.0.4
+ distro==1.9.0
+ exceptiongroup==1.3.0
+ fastmcp==2.9.0
+ h11==0.16.0
+ httpcore==1.0.9
+ httpx==0.28.1
+ httpx-sse==0.4.0
+ idna==3.10
...
+ requests==2.32.4
+ rich==14.0.0
+ shellingham==1.5.4
+ sniffio==1.3.1
+ sse-starlette==2.3.6
+ starlette==0.47.1
+ typer==0.16.0
+ typing-extensions==4.14.0
+ typing-inspection==0.4.1
+ typing-inspection==0.4.1

Vamos usar o Sonnet 3.5 como modelo LLM, então criamos um arquivo .env na pasta do cliente com a API Key do Claude que pode ser obtida na página keys da API do Claude

	
< > Input
Python
%%writefile client_MCP/.env
ANTHROPIC_API_KEY="ANTHROPIC_API_KEY"
Copied
>_ Output
			
Writing client_MCP/.env

MCP básicolink image 58

Escrevemos o mínimo código de que precisamos para ter um servidor MCP

	
< > Input
Python
%%writefile gitHub_MCP_server/github_server.py
from mcp.server.fastmcp import FastMCP
# Create an MCP server
mcp = FastMCP("GitHubMCP")
if __name__ == "__main__":
# Initialize and run the server
mcp.run(transport='stdio')
Copied
>_ Output
			
Overwriting gitHub_MCP_server/github_server.py

Como se pode ver, temos que criar um objeto FastMCP e depois executar o servidor com mcp.run.

Biblioteca com funções para ler do GitHublink image 59

Como vamos a criar um servidor MCP para poder usar utilidades do GitHub, vamos criar um arquivo com as funções necessárias para construir os headers necessários para poder usar a API do GitHub.

	
< > Input
Python
%%writefile gitHub_MCP_server/github.py
import os
from dotenv import load_dotenv
# Load the GitHub token from the .env file
load_dotenv()
GITHUB_TOKEN = os.environ.get("GITHUB_TOKEN")
# Check if the GitHub token is configured
if not GITHUB_TOKEN:
print("WARNING: The GITHUB_TOKEN environment variable is not configured.")
print("Requests to the GitHub API may fail due to rate limits.")
print("Create a .env file in this directory with GITHUB_TOKEN='your_token_here'")
raise ValueError("GITHUB_TOKEN is not configured")
# Helper function to create headers for GitHub API requests
def create_github_headers():
headers = {}
if GITHUB_TOKEN:
headers["Authorization"] = f"Bearer {GITHUB_TOKEN}"
# GitHub recommends including a User-Agent
headers["User-Agent"] = "MCP_GitHub_Server_Example"
headers["Accept"] = "application/vnd.github.v3+json" # Good practice
return headers
Copied
>_ Output
			
Overwriting gitHub_MCP_server/github.py

Para poder construir os headers, precisamos de um token do GitHub. Para isso, vamos a personal-access-tokens e criamos um novo token. Copiamo-lo

Agora, criamos um .env, onde vamos armazenar o token do GitHub.

	
< > Input
Python
%%writefile gitHub_MCP_server/.env
GITHUB_TOKEN = "GITHUB_TOKEN"
Copied
>_ Output
			
Overwriting gitHub_MCP_server/.env

Criar tool de MCP para obter uma lista de issues de um repositório do GitHublink image 60

Servidor MCPlink image 61

Adicionamos uma função para poder listar os issues de um repositório do GitHub. Para converter essa função em uma tool de MCP, usamos o decorador @mcp.tool()

	
< > Input
Python
%%writefile gitHub_MCP_server/github_server.py
import httpx
from fastmcp import FastMCP
from github import GITHUB_TOKEN, create_github_headers
# Create a FastMCP server
mcp = FastMCP("GitHubMCP")
@mcp.tool()
async def list_repository_issues(owner: str, repo_name: str) -&gt; list[dict]:
"""
Lists open issues for a given GitHub repository.
Args:
owner: The owner of the repository (e.g., 'modelcontextprotocol')
repo_name: The name of the repository (e.g., 'python-sdk')
Returns:
list[dict]: A list of dictionaries, each containing information about an issue
"""
# Limit to the first 10 issues to avoid long responses
api_url = f"https://api.github.com/repos/{owner}/{repo_name}/issues?state=open&amp;per_page=10"
print(f"Fetching issues from {api_url}...")
async with httpx.AsyncClient() as client:
try:
response = await client.get(api_url, headers=create_github_headers())
response.raise_for_status()
issues_data = response.json()
if not issues_data:
print("No open issues found for this repository.")
return [{"message": "No open issues found for this repository."}]
issues_summary = []
for issue in issues_data:
# Create a more concise summary
summary = f"#{issue.get('number', 'N/A')}: {issue.get('title', 'Sin título')}"
if issue.get('comments', 0) &gt; 0:
summary += f" ({issue.get('comments')} comentarios)"
issues_summary.append({
"number": issue.get("number"),
"title": issue.get("title"),
"user": issue.get("user", {}).get("login"),
"url": issue.get("html_url"),
"comments": issue.get("comments"),
"summary": summary
})
print(f"Found {len(issues_summary)} open issues.")
# Add context information
result = {
"total_found": len(issues_summary),
"repository": f"{owner}/{repo_name}",
"note": "Mostrando los primeros 10 issues abiertos" if len(issues_summary) == 10 else f"Mostrando todos los {len(issues_summary)} issues abiertos",
"issues": issues_summary
}
return [result]
except httpx.HTTPStatusError as e:
error_message = e.response.json().get("message", "No additional message from API.")
if e.response.status_code == 403 and GITHUB_TOKEN:
error_message += " (Rate limit with token or token lacks permissions?)"
elif e.response.status_code == 403 and not GITHUB_TOKEN:
error_message += " (Rate limit without token. Consider creating a .env file with GITHUB_TOKEN.)"
print(f"GitHub API error: {e.response.status_code}. {error_message}")
return [{
"error": f"GitHub API error: {e.response.status_code}",
"message": error_message
}]
except Exception as e:
print(f"An unexpected error occurred: {str(e)}")
return [{"error": f"An unexpected error occurred: {str(e)}"}]
if __name__ == "__main__":
print("DEBUG: Starting GitHub FastMCP server...")
print(f"DEBUG: Server name: {mcp.name}")
print("DEBUG: Available tools: list_repository_issues")
# Initialize and run the server
mcp.run()
Copied
>_ Output
			
Overwriting gitHub_MCP_server/github_server.py

Cliente MCPlink image 62

Agora criamos um cliente MCP para poder usar a tool que criamos

	
< > Input
Python
%%writefile client_MCP/client.py
import sys
import asyncio
from contextlib import AsyncExitStack
from anthropic import Anthropic
from dotenv import load_dotenv
from fastmcp import Client
# Load environment variables from .env file
load_dotenv()
class FastMCPClient:
"""
FastMCP client that integrates with Claude to process user queries
and use tools exposed by a FastMCP server.
"""
def __init__(self):
"""Initialize the FastMCP client with Anthropic and resource management."""
self.exit_stack = AsyncExitStack()
self.anthropic = Anthropic()
self.client = None
async def connect_to_server(self, server_script_path: str):
"""
Connect to the specified FastMCP server.
Args:
server_script_path: Path to the server script (Python)
"""
print(f"🔗 Connecting to FastMCP server: {server_script_path}")
# Determine the server type based on the extension
if not server_script_path.endswith('.py'):
raise ValueError(f"Unsupported server type. Use .py files. Received: {server_script_path}")
# Create FastMCP client
self.client = Client(server_script_path)
# Note: FastMCP Client automatically infers transport from .py files
print("✅ Client created successfully")
async def list_available_tools(self):
"""List available tools in the FastMCP server."""
try:
# Get list of tools from the server using FastMCP context
async with self.client as client:
tools = await client.list_tools()
if tools:
print(f" 🛠️ Available tools ({len(tools)}):")
print("=" * 50)
for tool in tools:
print(f"📋 {tool.name}")
if tool.description:
print(f" Description: {tool.description}")
# Show parameters if available
if hasattr(tool, 'inputSchema') and tool.inputSchema:
if 'properties' in tool.inputSchema:
params = list(tool.inputSchema['properties'].keys())
print(f" Parameters: {', '.join(params)}")
print()
else:
print("⚠️ No tools found in the server")
except Exception as e:
print(f"❌ Error listing tools: {str(e)}")
async def process_query(self, query: str) -&gt; str:
"""
Process a user query, interacting with Claude and FastMCP tools.
Args:
query: User query
Returns:
str: Final processed response
"""
try:
# Use FastMCP context for all operations
async with self.client as client:
# Get available tools
tools_list = await client.list_tools()
# Prepare tools for Claude in correct format
claude_tools = []
for tool in tools_list:
claude_tool = {
"name": tool.name,
"description": tool.description or f"Tool {tool.name}",
"input_schema": tool.inputSchema or {"type": "object", "properties": {}}
}
claude_tools.append(claude_tool)
# Create initial message for Claude
messages = [
{
"role": "user",
"content": query
}
]
# First call to Claude
response = self.anthropic.messages.create(
model="claude-3-5-sonnet-20241022",
max_tokens=6000,
messages=messages,
tools=claude_tools if claude_tools else None
)
# Process Claude's response
response_text = ""
for content_block in response.content:
if content_block.type == "text":
response_text += content_block.text
elif content_block.type == "tool_use":
# Claude wants to use a tool
tool_name = content_block.name
tool_args = content_block.input
tool_call_id = content_block.id
print(f"🔧 Claude wants to use: {tool_name}")
print(f"📝 Arguments: {tool_args}")
try:
# Execute tool on the FastMCP server
tool_result = await client.call_tool(tool_name, tool_args)
print(f"✅ Tool executed successfully")
# Add tool result to the conversation
messages.append({
"role": "assistant",
"content": response.content
})
# Format result for Claude
if tool_result:
# Convert result to string format for Claude
result_content = str(tool_result)
messages.append({
"role": "user",
"content": [{
"type": "tool_result",
"tool_use_id": tool_call_id,
"content": f"Tool result: {result_content}"
}]
})
else:
messages.append({
"role": "user",
"content": [{
"type": "tool_result",
"tool_use_id": tool_call_id,
"content": "Tool executed without response content"
}]
})
# Second call to Claude with the tool result
final_response = self.anthropic.messages.create(
model="claude-3-5-sonnet-20241022",
max_tokens=6000,
messages=messages,
tools=claude_tools if claude_tools else None
)
# Extract text from the final response
for final_content in final_response.content:
if final_content.type == "text":
response_text += final_content.text
except Exception as e:
error_msg = f"❌ Error executing {tool_name}: {str(e)}"
print(error_msg)
response_text += f" {error_msg}"
return response_text
except Exception as e:
error_msg = f"❌ Error processing query: {str(e)}"
print(error_msg)
return error_msg
async def chat_loop(self):
"""
Main chat loop with user interaction.
"""
print(" 🤖 FastMCP client started. Write 'quit', 'q', 'exit', 'salir' to exit.")
print("💬 You can ask questions about GitHub repositories!")
print("📚 The client can use tools from the FastMCP server")
print("-" * 60)
while True:
try:
# Request user input
user_input = input(" 👤 You: ").strip()
if user_input.lower() in ['quit', 'q', 'exit', 'salir']:
print("👋 Bye!")
break
if not user_input:
continue
print(" 🤔 Claude is thinking...")
# Process query
response = await self.process_query(user_input)
# Show response
print(f" 🤖 Claude: {response}")
except KeyboardInterrupt:
print(" 👋 Disconnecting...")
break
except Exception as e:
print(f" ❌ Error in chat: {str(e)}")
continue
async def cleanup(self):
"""Clean up resources and close connections."""
print("🧹 Cleaning up resources...")
# FastMCP Client cleanup is handled automatically by context manager
await self.exit_stack.aclose()
print("✅ Resources released")
async def main():
"""
Main function that initializes and runs the FastMCP client.
"""
# Verify command line arguments
if len(sys.argv) != 2:
print("❌ Usage: python client.py &lt;path_to_fastmcp_server&gt;")
print("📝 Example: python client.py ../MCP_github/github_server.py")
sys.exit(1)
server_script_path = sys.argv[1]
# Create and run client
client = FastMCPClient()
try:
# Connect to the server
await client.connect_to_server(server_script_path)
# List available tools after connection
await client.list_available_tools()
# Start chat loop
await client.chat_loop()
except Exception as e:
print(f"❌ Fatal error: {str(e)}")
finally:
# Ensure resources are cleaned up
await client.cleanup()
if __name__ == "__main__":
# Entry point of the script
asyncio.run(main())
Copied
>_ Output
			
Overwriting client_MCP/client.py

Explicação do cliente MCP

  • Em main verifica-se que foi passado um argumento com o caminho do servidor MCP.
  • Cria-se um objeto da classe FastMCPClient com o caminho do servidor MCP. Ao criar o objeto, é executado o método __init__, que cria a conexão com o LLM da Anthropic, que será o LLM que vai colocar o "cérebro"
  • Tenta-se conectar com o servidor MCP chamando o método connect_to_server para abrir uma sessão com o servidor MCP.
  • As tools disponíveis são listadas com o método list_available_tools
  • Se foi possível conectar, chama-se o método chat_loop, que é um loop infinito para conversar com o LLM que acabou de ser criado no cliente. A execução só é interrompida quando se introduz quit, q, exit ou salir no chat.
  • A entrada do usuário é processada com o método process_query, que obtém a lista de tools disponíveis e faz uma solicitação ao LLM com a mensagem do usuário e a lista de tools
  • Se o LLM responder com texto, o texto é retornado, que será impresso
  • Se o LLM responder com tool_use, obtém-se o nome da tool, os argumentos e cria-se um ID de execução. A ferramenta é executada. Com o resultado da ferramenta, cria-se uma nova mensagem que é enviada ao LLM para que a processe e gere uma resposta, que será devolvida e impressa.
  • Quando a conversa terminar, o método cleanup será chamado, que fechará tudo o que for necessário fechar.

Teste da toollink image 63

Vamos para a rota do cliente e o executamos, fornecendo a rota do servidor MCP.

	
< > Input
Python
!cd client_MCP && source .venv/bin/activate && uv run client.py ../gitHub_MCP_server/github_server.py
Copied
>_ Output
			
🔗 Connecting to FastMCP server: ../gitHub_MCP_server/github_server.py
✅ Client created successfully
[06/28/25 09:22:09] INFO Starting MCP server 'GitHubMCP' with transport 'stdio' server.py:1246
🛠️ Available tools (1):
==================================================
📋 list_repository_issues
Description: Lists open issues for a given GitHub repository.
Args:
owner: The owner of the repository (e.g., 'modelcontextprotocol')
repo_name: The name of the repository (e.g., 'python-sdk')
Returns:
list[dict]: A list of dictionaries, each containing information about an issue
Parameters: owner, repo_name
🤖 FastMCP client started. Write 'quit', 'q', 'exit', 'salir' to exit.
💬 You can ask questions about GitHub repositories!
📚 The client can use tools from the FastMCP server
------------------------------------------------------------
👤 You: Tell me de issues of repository transformers of huggingface
🤔 Claude is thinking...
🔧 Claude wants to use: list_repository_issues
📝 Arguments: {'owner': 'huggingface', 'repo_name': 'transformers'}
✅ Tool executed successfully
🤖 Claude: I'll help you list the issues from the Hugging Face transformers repository. Let me use the `list_repository_issues` function with the appropriate parameters.I'll summarize the current open issues from the Hugging Face transformers repository. Here are the 10 most recent open issues:
1. [#39097] Core issue about saving models with multiple shared tensor groups when dispatched
2. [#39096] Pull request to fix position index in v4.52.4
3. [#39095] Issue with Qwen2_5_VLVisionAttention flash attention missing 'is_causal' attribute
4. [#39094] Documentation improvement for PyTorch examples
5. [#39093] Style change PR for lru_cache decorator
6. [#39091] Compatibility issue with sentencepiece on Windows in Python 3.13
7. [#39090] Pull request for fixing bugs in finetune and batch inference
8. [#39089] Bug report for LlavaOnevisonConfig initialization in version 4.52.4
9. [#39087] Documentation PR for Gemma 3n audio encoder
10. [#39084] Pull request for refactoring gemma3n
Note that this is showing the 10 most recent open issues, and there might be more issues in the repository. Each issue has a link where you can find more details about the specific problem or proposed changes.
Would you like more specific information about any of these issues?
👤 You: q
👋 Bye!
🧹 Cleaning up resources...
✅ Resources released

Ao executá-lo, vemos

🛠️  Ferramentas disponíveis (1):
==================================================
📋 listar_issues_do_repositório
Descrição: Lista os problemas abertos de um determinado repositório do GitHub.

Args:
owner: O proprietário do repositório (por exemplo, 'modelcontextprotocol')
repo_name: O nome do repositório (por exemplo, 'python-sdk')
Retornos:
list[dict]: Uma lista de dicionários, cada um contendo informações sobre um problema
Parâmetros: owner, repo_name

O que indica que o cliente MCP pode ver a tool que criamos no servidor MCP.

Depois podemos ver

👤 Você: Me conte os problemas do repositório transformers da huggingface

🤔 Claude está pensando...
🔧 Chamando ferramenta: list_repository_issues
📝 Argumentos: {'owner': 'huggingface', 'repo_name': 'transformers'}
✅ Ferramenta executada com sucesso

Pedimos os issues do repositório transformers da huggingface. Após pensar um pouco, nos diz que vai usar a tool list_repository_issues com os argumentos {'owner': 'huggingface', 'repo_name': 'transformers'}.

Por fim, ele nos diz que a tool foi executada corretamente.

Por fim, com o resultado de executar a tool, Claude o processa e nos cria uma resposta com a lista de issues.

🤖 Claude: Vou ajudar você a listar os problemas do repositório Hugging Face transformers. Vou usar a função `list_repository_issues` com os parâmetros apropriados. Vou resumir os problemas abertos atuais do repositório Hugging Face transformers. Aqui estão os 10 problemas abertos mais recentes:

1. [#39097] Problema central sobre salvar modelos com múltiplos grupos de tensores compartilhados quando despachados
2. [#39096] Pull request para corrigir o índice de posição no v4.52.4
3. [#39095] Problema con Qwen2_5_VLVisionAttention: falta el atributo `is_causal` en flash attention
4. [#39094] Melhoria da documentação para exemplos do PyTorch
5. [#39093] PR de alteração de estilo para o decorador lru_cache
6. [#39091] Problema de compatibilidade com sentencepiece no Windows em Python 3.13
7. [#39090] Pull request para corrigir bugs em fine-tuning e inferência em lote8. [#39089] Informe de error sobre la inicialización de LlavaOnevisonConfig en la versión 4.52.4
9. [#39087] PR de documentação para o codificador de áudio do Gemma 3n
10. [#39084] Pull request para refatoração do gemma3n

Observe que isto mostra os 10 problemas em aberto mais recentes, e pode haver mais problemas no repositório. Cada problema tem um link onde você pode encontrar mais detalhes sobre o problema específico ou as alterações propostas.

Gostaria de mais informações específicas sobre algum destes problemas?

Criar o servidor MCP com mais informaçõeslink image 64

Servidor MCPlink image 65

Antes, criamos o servidor com mcp = FastMCP(), mas podemos aproveitar para dar a ele um nome e uma descrição com o servidor com

mcp = FastMCP(
name="GitHubMCP",
instructions="""
Este servidor fornece ferramentas, recursos e prompts para interagir com a API do GitHub.

markdown

"""

)
	
< > Input
Python
%%writefile gitHub_MCP_server/github_server.py
import httpx
from typing import Optional
from fastmcp import FastMCP
from github import GITHUB_TOKEN, create_github_headers
# Create FastMCP server
mcp = FastMCP(
name="GitHubMCP",
instructions="""
This server provides tools, resources and prompts to interact with the GitHub API.
"""
)
@mcp.tool()
async def list_repository_issues(owner: str, repo_name: str) -&gt; list[dict]:
"""
Lists open issues for a given GitHub repository.
Args:
owner: The owner of the repository (e.g., 'modelcontextprotocol')
repo_name: The name of the repository (e.g., 'python-sdk')
ctx: The MCP context for logging.
Returns:
list[dict]: A list of dictionaries, each containing information about an issue
"""
# Limit to first 10 issues to avoid very long responses
api_url = f"https://api.github.com/repos/{owner}/{repo_name}/issues?state=open&amp;per_page=10"
print(f"Fetching issues from {api_url}...")
async with httpx.AsyncClient() as client:
try:
response = await client.get(api_url, headers=create_github_headers())
response.raise_for_status()
issues_data = response.json()
if not issues_data:
print("No open issues found for this repository.")
return [{"message": "No open issues found for this repository."}]
issues_summary = []
for issue in issues_data:
# Create a more concise summary
summary = f"#{issue.get('number', 'N/A')}: {issue.get('title', 'No title')}"
if issue.get('comments', 0) &gt; 0:
summary += f" ({issue.get('comments')} comments)"
issues_summary.append({
"number": issue.get("number"),
"title": issue.get("title"),
"user": issue.get("user", {}).get("login"),
"url": issue.get("html_url"),
"comments": issue.get("comments"),
"summary": summary
})
print(f"Found {len(issues_summary)} open issues.")
# Add context information
result = {
"total_found": len(issues_summary),
"repository": f"{owner}/{repo_name}",
"note": "Showing first 10 open issues" if len(issues_summary) == 10 else f"Showing all {len(issues_summary)} open issues",
"issues": issues_summary
}
return [result]
except httpx.HTTPStatusError as e:
error_message = e.response.json().get("message", "No additional message from API.")
if e.response.status_code == 403 and GITHUB_TOKEN:
error_message += " (Rate limit with token or token lacks permissions?)"
elif e.response.status_code == 403 and not GITHUB_TOKEN:
error_message += " (Rate limit without token. Consider creating a .env file with GITHUB_TOKEN.)"
print(f"GitHub API error: {e.response.status_code}. {error_message}")
return [{
"error": f"GitHub API error: {e.response.status_code}",
"message": error_message
}]
except Exception as e:
print(f"An unexpected error occurred: {str(e)}")
return [{"error": f"An unexpected error occurred: {str(e)}"}]
if __name__ == "__main__":
print("DEBUG: Starting FastMCP GitHub server...")
print(f"DEBUG: Server name: {mcp.name}")
# Initialize and run the server
mcp.run()
Copied
>_ Output
			
Overwriting gitHub_MCP_server/github_server.py

Filtrar tools mediante tagslink image 66

Servidor MCPlink image 67

MCP nos dá a opção de expor tools por meio de tags, o que pode ser útil para expor apenas tools para depuração, para que somente determinados usuários possam usá-las, etc.

Para isso, quando criamos o servidor MCP, indicamos as tags que queremos incluir

mcp = FastMCP(
name="GitHubMCP",
instructions="Este servidor fornece ferramentas, recursos e prompts para interagir com a API do GitHub.",
include_tags={"public"}
)

E depois, quando criamos a tool, podemos indicar as tags que queremos que ela tenha.

@mcp.tool(tags=production)

Vamos ver um exemplo

	
< > Input
Python
%%writefile gitHub_MCP_server/github_server.py
import httpx
from typing import Optional
from fastmcp import FastMCP
from github import GITHUB_TOKEN, create_github_headers
# Create FastMCP server
mcp = FastMCP(
name="GitHubMCP",
instructions="This server provides tools, resources and prompts to interact with the GitHub API.",
include_tags={"public"}
)
@mcp.tool(tags={"public", "production"})
async def list_repository_issues(owner: str, repo_name: str) -&gt; list[dict]:
"""
Lists open issues for a given GitHub repository.
Args:
owner: The owner of the repository (e.g., 'modelcontextprotocol')
repo_name: The name of the repository (e.g., 'python-sdk')
Returns:
list[dict]: A list of dictionaries, each containing information about an issue
"""
# Limit to first 10 issues to avoid very long responses
api_url = f"https://api.github.com/repos/{owner}/{repo_name}/issues?state=open&amp;per_page=10"
print(f"Fetching issues from {api_url}...")
async with httpx.AsyncClient() as client:
try:
response = await client.get(api_url, headers=create_github_headers())
response.raise_for_status()
issues_data = response.json()
if not issues_data:
print("No open issues found for this repository.")
return [{"message": "No open issues found for this repository."}]
issues_summary = []
for issue in issues_data:
# Create a more concise summary
summary = f"#{issue.get('number', 'N/A')}: {issue.get('title', 'No title')}"
if issue.get('comments', 0) &gt; 0:
summary += f" ({issue.get('comments')} comments)"
issues_summary.append({
"number": issue.get("number"),
"title": issue.get("title"),
"user": issue.get("user", {}).get("login"),
"url": issue.get("html_url"),
"comments": issue.get("comments"),
"summary": summary
})
print(f"Found {len(issues_summary)} open issues.")
# Add context information
result = {
"total_found": len(issues_summary),
"repository": f"{owner}/{repo_name}",
"note": "Showing first 10 open issues" if len(issues_summary) == 10 else f"Showing all {len(issues_summary)} open issues",
"issues": issues_summary
}
return [result]
except httpx.HTTPStatusError as e:
error_message = e.response.json().get("message", "No additional message from API.")
if e.response.status_code == 403 and GITHUB_TOKEN:
error_message += " (Rate limit with token or token lacks permissions?)"
elif e.response.status_code == 403 and not GITHUB_TOKEN:
error_message += " (Rate limit without token. Consider creating a .env file with GITHUB_TOKEN.)"
print(f"GitHub API error: {e.response.status_code}. {error_message}")
return [{
"error": f"GitHub API error: {e.response.status_code}",
"message": error_message
}]
except Exception as e:
print(f"An unexpected error occurred: {str(e)}")
return [{"error": f"An unexpected error occurred: {str(e)}"}]
@mcp.tool(tags={"private", "development"})
async def list_more_repository_issues(owner: str, repo_name: str) -&gt; list[dict]:
"""
Lists open issues for a given GitHub repository.
Args:
owner: The owner of the repository (e.g., 'modelcontextprotocol')
repo_name: The name of the repository (e.g., 'python-sdk')
Returns:
list[dict]: A list of dictionaries, each containing information about an issue
"""
# Limit to first 100 issues to avoid very long responses
api_url = f"https://api.github.com/repos/{owner}/{repo_name}/issues?state=open&amp;per_page=100"
print(f"Fetching issues from {api_url}...")
async with httpx.AsyncClient() as client:
try:
response = await client.get(api_url, headers=create_github_headers())
response.raise_for_status()
issues_data = response.json()
if not issues_data:
print("No open issues found for this repository.")
return [{"message": "No open issues found for this repository."}]
issues_summary = []
for issue in issues_data:
# Create a more concise summary
summary = f"#{issue.get('number', 'N/A')}: {issue.get('title', 'No title')}"
if issue.get('comments', 0) &gt; 0:
summary += f" ({issue.get('comments')} comments)"
issues_summary.append({
"number": issue.get("number"),
"title": issue.get("title"),
"user": issue.get("user", {}).get("login"),
"url": issue.get("html_url"),
"comments": issue.get("comments"),
"summary": summary
})
print(f"Found {len(issues_summary)} open issues.")
# Add context information
result = {
"total_found": len(issues_summary),
"repository": f"{owner}/{repo_name}",
"note": "Showing first 10 open issues" if len(issues_summary) == 10 else f"Showing all {len(issues_summary)} open issues",
"issues": issues_summary
}
return [result]
except httpx.HTTPStatusError as e:
error_message = e.response.json().get("message", "No additional message from API.")
if e.response.status_code == 403 and GITHUB_TOKEN:
error_message += " (Rate limit with token or token lacks permissions?)"
elif e.response.status_code == 403 and not GITHUB_TOKEN:
error_message += " (Rate limit without token. Consider creating a .env file with GITHUB_TOKEN.)"
print(f"GitHub API error: {e.response.status_code}. {error_message}")
return [{
"error": f"GitHub API error: {e.response.status_code}",
"message": error_message
}]
except Exception as e:
print(f"An unexpected error occurred: {str(e)}")
return [{"error": f"An unexpected error occurred: {str(e)}"}]
if __name__ == "__main__":
print("DEBUG: Starting FastMCP GitHub server...")
print(f"DEBUG: Server name: {mcp.name}")
# Initialize and run the server
mcp.run()
Copied
>_ Output
			
Overwriting gitHub_MCP_server/github_server.py

Podemos ver que criamos a função list_repository_issues, que lista apenas 10 issues e que tem as tags public e production. E criamos a função list_more_repository_issues, que lista 100 issues de um repositório e que tem as tags private e development.

Além disso, declaramos o servidor por meio de

mcp = FastMCP(
name="GitHubMCP",
instructions="Este servidor fornece ferramentas, recursos e prompts para interagir com a API do GitHub.",
include_tags={"public"})

Portanto, o cliente só terá acesso às tools que tenham a tag public, ou seja, a list_repository_issues. Só poderá ver uma lista de 10 issues.

Teste das tagslink image 68

Voltamos a executar o cliente MCP

	
< > Input
Python
!cd client_MCP && source .venv/bin/activate && uv run client.py ../gitHub_MCP_server/github_server.py
Copied
>_ Output
			
🔗 Connecting to FastMCP server: ../gitHub_MCP_server/github_server.py
✅ Client created successfully
[06/28/25 09:44:55] INFO Starting MCP server 'GitHubMCP' with ]8;id=896921;file:///Users/macm1/Documents/web/portafolio/posts/client_MCP/.venv/lib/python3.11/site-packages/fastmcp/server/server.py\server.py]8;;\:]8;id=507812;file:///Users/macm1/Documents/web/portafolio/posts/client_MCP/.venv/lib/python3.11/site-packages/fastmcp/server/server.py#1246\1246]8;;\
transport 'stdio'
🛠️ Available tools (1):
==================================================
📋 list_repository_issues
Description: Lists open issues for a given GitHub repository.
Args:
owner: The owner of the repository (e.g., 'modelcontextprotocol')
repo_name: The name of the repository (e.g., 'python-sdk')
ctx: The MCP context for logging.
Returns:
list[dict]: A list of dictionaries, each containing information about an issue
Parameters: owner, repo_name
🤖 FastMCP client started. Write 'quit', 'q', 'exit', 'salir' to exit.
💬 You can ask questions about GitHub repositories!
📚 The client can use tools from the FastMCP server
------------------------------------------------------------
👤 You:

Não é necessário fazer uma solicitação, pois vemos o seguinte:

🛠️  Ferramentas disponíveis (1):
==================================================
📋 list_repository_issues
Description: Lista problemas abertos para um determinado repositório do GitHub.

Args:
owner: O proprietário do repositório (por exemplo, 'modelcontextprotocol')
repo_name: O nome do repositório (por exemplo, 'python-sdk')
ctx: O contexto MCP para logging.

Retorna:
list[dict]: Uma lista de dicionários, cada um contendo informações sobre um issue
Parâmetros: owner, repo_name

Ou seja, o cliente só pode ver a tool list_repository_issues e não a tool list_all_repository_issues.

Alteração para privatelink image 69

Trocamos include_tags para private para usar a tool list_more_repository_issues

	
< > Input
Python
%%writefile gitHub_MCP_server/github_server.py
import httpx
from typing import Optional
from fastmcp import FastMCP
from github import GITHUB_TOKEN, create_github_headers
# Create FastMCP server
mcp = FastMCP(
name="GitHubMCP",
instructions="This server provides tools, resources and prompts to interact with the GitHub API.",
include_tags={"private"}
)
@mcp.tool(tags={"public", "production"})
async def list_repository_issues(owner: str, repo_name: str) -&gt; list[dict]:
"""
Lists open issues for a given GitHub repository.
Args:
owner: The owner of the repository (e.g., 'modelcontextprotocol')
repo_name: The name of the repository (e.g., 'python-sdk')
Returns:
list[dict]: A list of dictionaries, each containing information about an issue
"""
# Limit to first 10 issues to avoid very long responses
api_url = f"https://api.github.com/repos/{owner}/{repo_name}/issues?state=open&amp;per_page=10"
print(f"Fetching issues from {api_url}...")
async with httpx.AsyncClient() as client:
try:
response = await client.get(api_url, headers=create_github_headers())
response.raise_for_status()
issues_data = response.json()
if not issues_data:
print("No open issues found for this repository.")
return [{"message": "No open issues found for this repository."}]
issues_summary = []
for issue in issues_data:
# Create a more concise summary
summary = f"#{issue.get('number', 'N/A')}: {issue.get('title', 'No title')}"
if issue.get('comments', 0) &gt; 0:
summary += f" ({issue.get('comments')} comments)"
issues_summary.append({
"number": issue.get("number"),
"title": issue.get("title"),
"user": issue.get("user", {}).get("login"),
"url": issue.get("html_url"),
"comments": issue.get("comments"),
"summary": summary
})
print(f"Found {len(issues_summary)} open issues.")
# Add context information
result = {
"total_found": len(issues_summary),
"repository": f"{owner}/{repo_name}",
"note": "Showing first 10 open issues" if len(issues_summary) == 10 else f"Showing all {len(issues_summary)} open issues",
"issues": issues_summary
}
return [result]
except httpx.HTTPStatusError as e:
error_message = e.response.json().get("message", "No additional message from API.")
if e.response.status_code == 403 and GITHUB_TOKEN:
error_message += " (Rate limit with token or token lacks permissions?)"
elif e.response.status_code == 403 and not GITHUB_TOKEN:
error_message += " (Rate limit without token. Consider creating a .env file with GITHUB_TOKEN.)"
print(f"GitHub API error: {e.response.status_code}. {error_message}")
return [{
"error": f"GitHub API error: {e.response.status_code}",
"message": error_message
}]
except Exception as e:
print(f"An unexpected error occurred: {str(e)}")
return [{"error": f"An unexpected error occurred: {str(e)}"}]
@mcp.tool(tags={"private", "development"})
async def list_more_repository_issues(owner: str, repo_name: str) -&gt; list[dict]:
"""
Lists open issues for a given GitHub repository.
Args:
owner: The owner of the repository (e.g., 'modelcontextprotocol')
repo_name: The name of the repository (e.g., 'python-sdk')
Returns:
list[dict]: A list of dictionaries, each containing information about an issue
"""
# Limit to first 100 issues to avoid very long responses
api_url = f"https://api.github.com/repos/{owner}/{repo_name}/issues?state=open&amp;per_page=100"
print(f"Fetching issues from {api_url}...")
async with httpx.AsyncClient() as client:
try:
response = await client.get(api_url, headers=create_github_headers())
response.raise_for_status()
issues_data = response.json()
if not issues_data:
print("No open issues found for this repository.")
return [{"message": "No open issues found for this repository."}]
issues_summary = []
for issue in issues_data:
# Create a more concise summary
summary = f"#{issue.get('number', 'N/A')}: {issue.get('title', 'No title')}"
if issue.get('comments', 0) &gt; 0:
summary += f" ({issue.get('comments')} comments)"
issues_summary.append({
"number": issue.get("number"),
"title": issue.get("title"),
"user": issue.get("user", {}).get("login"),
"url": issue.get("html_url"),
"comments": issue.get("comments"),
"summary": summary
})
print(f"Found {len(issues_summary)} open issues.")
# Add context information
result = {
"total_found": len(issues_summary),
"repository": f"{owner}/{repo_name}",
"note": "Showing first 10 open issues" if len(issues_summary) == 10 else f"Showing all {len(issues_summary)} open issues",
"issues": issues_summary
}
return [result]
except httpx.HTTPStatusError as e:
error_message = e.response.json().get("message", "No additional message from API.")
if e.response.status_code == 403 and GITHUB_TOKEN:
error_message += " (Rate limit with token or token lacks permissions?)"
elif e.response.status_code == 403 and not GITHUB_TOKEN:
error_message += " (Rate limit without token. Consider creating a .env file with GITHUB_TOKEN.)"
print(f"GitHub API error: {e.response.status_code}. {error_message}")
return [{
"error": f"GitHub API error: {e.response.status_code}",
"message": error_message
}]
except Exception as e:
print(f"An unexpected error occurred: {str(e)}")
return [{"error": f"An unexpected error occurred: {str(e)}"}]
if __name__ == "__main__":
print("DEBUG: Starting FastMCP GitHub server...")
print(f"DEBUG: Server name: {mcp.name}")
# Initialize and run the server
mcp.run()
Copied
>_ Output
			
Overwriting gitHub_MCP_server/github_server.py

Teste da tag privatelink image 70

Executamos novamente o cliente com a alteração feita

	
< > Input
Python
!cd client_MCP && source .venv/bin/activate && uv run client.py ../gitHub_MCP_server/github_server.py
Copied
>_ Output
			
🔗 Connecting to FastMCP server: ../gitHub_MCP_server/github_server.py
✅ Client created successfully
[06/28/25 09:51:48] INFO Starting MCP server 'GitHubMCP' with ]8;id=921531;file:///Users/macm1/Documents/web/portafolio/posts/client_MCP/.venv/lib/python3.11/site-packages/fastmcp/server/server.py\server.py]8;;\:]8;id=418078;file:///Users/macm1/Documents/web/portafolio/posts/client_MCP/.venv/lib/python3.11/site-packages/fastmcp/server/server.py#1246\1246]8;;\
transport 'stdio'
🛠️ Available tools (1):
==================================================
📋 list_more_repository_issues
Description: Lists open issues for a given GitHub repository.
Args:
owner: The owner of the repository (e.g., 'modelcontextprotocol')
repo_name: The name of the repository (e.g., 'python-sdk')
Returns:
list[dict]: A list of dictionaries, each containing information about an issue
Parameters: owner, repo_name
🤖 FastMCP client started. Write 'quit', 'q', 'exit', 'salir' to exit.
💬 You can ask questions about GitHub repositories!
📚 The client can use tools from the FastMCP server
------------------------------------------------------------
👤 You:

Tal como antes, não é necessário fazer uma solicitação, já que nos mostra as tools disponíveis e vemos que temos list_more_repository_issues.

🛠️  Ferramentas disponíveis (1):
==================================================
📋 listar_mais_problemas_do_repositório
Descrição: Lista os problemas abertos de um determinado repositório do GitHub.

Args:
owner: O proprietário do repositório (por exemplo, 'modelcontextprotocol')repo_name: O nome do repositório (por exemplo, 'python-sdk')

Retorna:
list[dict]: Uma lista de dicionários, cada um contendo informações sobre um problema
Parâmetros: owner, repo_name

Retorno à publiclink image 71

Voltamos a definir include_tags como public para usar a tool list_repository_issues

	
< > Input
Python
%%writefile gitHub_MCP_server/github_server.py
import httpx
from typing import Optional
from fastmcp import FastMCP
from github import GITHUB_TOKEN, create_github_headers
# Create FastMCP server
mcp = FastMCP(
name="GitHubMCP",
instructions="This server provides tools, resources and prompts to interact with the GitHub API.",
include_tags={"public"}
)
@mcp.tool(tags={"public", "production"})
async def list_repository_issues(owner: str, repo_name: str) -&gt; list[dict]:
"""
Lists open issues for a given GitHub repository.
Args:
owner: The owner of the repository (e.g., 'modelcontextprotocol')
repo_name: The name of the repository (e.g., 'python-sdk')
Returns:
list[dict]: A list of dictionaries, each containing information about an issue
"""
# Limit to first 10 issues to avoid very long responses
api_url = f"https://api.github.com/repos/{owner}/{repo_name}/issues?state=open&amp;per_page=10"
print(f"Fetching issues from {api_url}...")
async with httpx.AsyncClient() as client:
try:
response = await client.get(api_url, headers=create_github_headers())
response.raise_for_status()
issues_data = response.json()
if not issues_data:
print("No open issues found for this repository.")
return [{"message": "No open issues found for this repository."}]
issues_summary = []
for issue in issues_data:
# Create a more concise summary
summary = f"#{issue.get('number', 'N/A')}: {issue.get('title', 'No title')}"
if issue.get('comments', 0) &gt; 0:
summary += f" ({issue.get('comments')} comments)"
issues_summary.append({
"number": issue.get("number"),
"title": issue.get("title"),
"user": issue.get("user", {}).get("login"),
"url": issue.get("html_url"),
"comments": issue.get("comments"),
"summary": summary
})
print(f"Found {len(issues_summary)} open issues.")
# Add context information
result = {
"total_found": len(issues_summary),
"repository": f"{owner}/{repo_name}",
"note": "Showing first 10 open issues" if len(issues_summary) == 10 else f"Showing all {len(issues_summary)} open issues",
"issues": issues_summary
}
return [result]
except httpx.HTTPStatusError as e:
error_message = e.response.json().get("message", "No additional message from API.")
if e.response.status_code == 403 and GITHUB_TOKEN:
error_message += " (Rate limit with token or token lacks permissions?)"
elif e.response.status_code == 403 and not GITHUB_TOKEN:
error_message += " (Rate limit without token. Consider creating a .env file with GITHUB_TOKEN.)"
print(f"GitHub API error: {e.response.status_code}. {error_message}")
return [{
"error": f"GitHub API error: {e.response.status_code}",
"message": error_message
}]
except Exception as e:
print(f"An unexpected error occurred: {str(e)}")
return [{"error": f"An unexpected error occurred: {str(e)}"}]
@mcp.tool(tags={"private", "development"})
async def list_more_repository_issues(owner: str, repo_name: str) -&gt; list[dict]:
"""
Lists open issues for a given GitHub repository.
Args:
owner: The owner of the repository (e.g., 'modelcontextprotocol')
repo_name: The name of the repository (e.g., 'python-sdk')
Returns:
list[dict]: A list of dictionaries, each containing information about an issue
"""
# Limit to first 100 issues to avoid very long responses
api_url = f"https://api.github.com/repos/{owner}/{repo_name}/issues?state=open&amp;per_page=100"
print(f"Fetching issues from {api_url}...")
async with httpx.AsyncClient() as client:
try:
response = await client.get(api_url, headers=create_github_headers())
response.raise_for_status()
issues_data = response.json()
if not issues_data:
print("No open issues found for this repository.")
return [{"message": "No open issues found for this repository."}]
issues_summary = []
for issue in issues_data:
# Create a more concise summary
summary = f"#{issue.get('number', 'N/A')}: {issue.get('title', 'No title')}"
if issue.get('comments', 0) &gt; 0:
summary += f" ({issue.get('comments')} comments)"
issues_summary.append({
"number": issue.get("number"),
"title": issue.get("title"),
"user": issue.get("user", {}).get("login"),
"url": issue.get("html_url"),
"comments": issue.get("comments"),
"summary": summary
})
print(f"Found {len(issues_summary)} open issues.")
# Add context information
result = {
"total_found": len(issues_summary),
"repository": f"{owner}/{repo_name}",
"note": "Showing first 10 open issues" if len(issues_summary) == 10 else f"Showing all {len(issues_summary)} open issues",
"issues": issues_summary
}
return [result]
except httpx.HTTPStatusError as e:
error_message = e.response.json().get("message", "No additional message from API.")
if e.response.status_code == 403 and GITHUB_TOKEN:
error_message += " (Rate limit with token or token lacks permissions?)"
elif e.response.status_code == 403 and not GITHUB_TOKEN:
error_message += " (Rate limit without token. Consider creating a .env file with GITHUB_TOKEN.)"
print(f"GitHub API error: {e.response.status_code}. {error_message}")
return [{
"error": f"GitHub API error: {e.response.status_code}",
"message": error_message
}]
except Exception as e:
print(f"An unexpected error occurred: {str(e)}")
return [{"error": f"An unexpected error occurred: {str(e)}"}]
if __name__ == "__main__":
print("DEBUG: Starting FastMCP GitHub server...")
print(f"DEBUG: Server name: {mcp.name}")
# Initialize and run the server
mcp.run()
Copied
>_ Output
			
Overwriting gitHub_MCP_server/github_server.py

Excluir tools por tagslink image 72

Assim como antes filtramos as tools que podem ser usadas por tags, também podemos excluir tools por tags. Para isso, ao criar o servidor, é preciso adicionar o parâmetro exclude_tags com as tags que queremos excluir.

Servidor MCPlink image 73

Criamos uma nova tool e a excluímos por meio de tags

	
< > Input
Python
%%writefile gitHub_MCP_server/github_server.py
import httpx
from typing import Optional
from fastmcp import FastMCP
from github import GITHUB_TOKEN, create_github_headers
# Create FastMCP server
mcp = FastMCP(
name="GitHubMCP",
instructions="This server provides tools, resources and prompts to interact with the GitHub API.",
include_tags={"public"},
exclude_tags={"first_issue"}
)
@mcp.tool(tags={"public", "production"})
async def list_repository_issues(owner: str, repo_name: str) -&gt; list[dict]:
"""
Lists open issues for a given GitHub repository.
Args:
owner: The owner of the repository (e.g., 'modelcontextprotocol')
repo_name: The name of the repository (e.g., 'python-sdk')
Returns:
list[dict]: A list of dictionaries, each containing information about an issue
"""
# Limit to first 10 issues to avoid very long responses
api_url = f"https://api.github.com/repos/{owner}/{repo_name}/issues?state=open&amp;per_page=10"
print(f"Fetching issues from {api_url}...")
async with httpx.AsyncClient() as client:
try:
response = await client.get(api_url, headers=create_github_headers())
response.raise_for_status()
issues_data = response.json()
if not issues_data:
print("No open issues found for this repository.")
return [{"message": "No open issues found for this repository."}]
issues_summary = []
for issue in issues_data:
# Create a more concise summary
summary = f"#{issue.get('number', 'N/A')}: {issue.get('title', 'No title')}"
if issue.get('comments', 0) &gt; 0:
summary += f" ({issue.get('comments')} comments)"
issues_summary.append({
"number": issue.get("number"),
"title": issue.get("title"),
"user": issue.get("user", {}).get("login"),
"url": issue.get("html_url"),
"comments": issue.get("comments"),
"summary": summary
})
print(f"Found {len(issues_summary)} open issues.")
# Add context information
result = {
"total_found": len(issues_summary),
"repository": f"{owner}/{repo_name}",
"note": "Showing first 10 open issues" if len(issues_summary) == 10 else f"Showing all {len(issues_summary)} open issues",
"issues": issues_summary
}
return [result]
except httpx.HTTPStatusError as e:
error_message = e.response.json().get("message", "No additional message from API.")
if e.response.status_code == 403 and GITHUB_TOKEN:
error_message += " (Rate limit with token or token lacks permissions?)"
elif e.response.status_code == 403 and not GITHUB_TOKEN:
error_message += " (Rate limit without token. Consider creating a .env file with GITHUB_TOKEN.)"
print(f"GitHub API error: {e.response.status_code}. {error_message}")
return [{
"error": f"GitHub API error: {e.response.status_code}",
"message": error_message
}]
except Exception as e:
print(f"An unexpected error occurred: {str(e)}")
return [{"error": f"An unexpected error occurred: {str(e)}"}]
@mcp.tool(tags={"private", "development"})
async def list_more_repository_issues(owner: str, repo_name: str) -&gt; list[dict]:
"""
Lists open issues for a given GitHub repository.
Args:
owner: The owner of the repository (e.g., 'modelcontextprotocol')
repo_name: The name of the repository (e.g., 'python-sdk')
Returns:
list[dict]: A list of dictionaries, each containing information about an issue
"""
# Limit to first 100 issues to avoid very long responses
api_url = f"https://api.github.com/repos/{owner}/{repo_name}/issues?state=open&amp;per_page=100"
print(f"Fetching issues from {api_url}...")
async with httpx.AsyncClient() as client:
try:
response = await client.get(api_url, headers=create_github_headers())
response.raise_for_status()
issues_data = response.json()
if not issues_data:
print("No open issues found for this repository.")
return [{"message": "No open issues found for this repository."}]
issues_summary = []
for issue in issues_data:
# Create a more concise summary
summary = f"#{issue.get('number', 'N/A')}: {issue.get('title', 'No title')}"
if issue.get('comments', 0) &gt; 0:
summary += f" ({issue.get('comments')} comments)"
issues_summary.append({
"number": issue.get("number"),
"title": issue.get("title"),
"user": issue.get("user", {}).get("login"),
"url": issue.get("html_url"),
"comments": issue.get("comments"),
"summary": summary
})
print(f"Found {len(issues_summary)} open issues.")
# Add context information
result = {
"total_found": len(issues_summary),
"repository": f"{owner}/{repo_name}",
"note": "Showing first 10 open issues" if len(issues_summary) == 10 else f"Showing all {len(issues_summary)} open issues",
"issues": issues_summary
}
return [result]
except httpx.HTTPStatusError as e:
error_message = e.response.json().get("message", "No additional message from API.")
if e.response.status_code == 403 and GITHUB_TOKEN:
error_message += " (Rate limit with token or token lacks permissions?)"
elif e.response.status_code == 403 and not GITHUB_TOKEN:
error_message += " (Rate limit without token. Consider creating a .env file with GITHUB_TOKEN.)"
print(f"GitHub API error: {e.response.status_code}. {error_message}")
return [{
"error": f"GitHub API error: {e.response.status_code}",
"message": error_message
}]
except Exception as e:
print(f"An unexpected error occurred: {str(e)}")
return [{"error": f"An unexpected error occurred: {str(e)}"}]
@mcp.tool(tags={"public", "first_issue"})
async def first_repository_issue(owner: str, repo_name: str) -&gt; list[dict]:
"""
Gets the first issue ever created in a GitHub repository.
Args:
owner: The owner of the repository (e.g., 'modelcontextprotocol')
repo_name: The name of the repository (e.g., 'python-sdk')
Returns:
list[dict]: A list containing information about the first issue created
"""
# Get the first issue by sorting by creation date in ascending order
api_url = f"https://api.github.com/repos/{owner}/{repo_name}/issues?state=all&amp;sort=created&amp;direction=asc&amp;per_page=1"
print(f"Fetching first issue from {api_url}...")
async with httpx.AsyncClient() as client:
try:
response = await client.get(api_url, headers=create_github_headers())
response.raise_for_status()
issues_data = response.json()
if not issues_data:
print("No issues found for this repository.")
return [{"message": "No issues found for this repository."}]
first_issue = issues_data[0]
# Create a detailed summary of the first issue
summary = f"#{first_issue.get('number', 'N/A')}: {first_issue.get('title', 'No title')}"
if first_issue.get('comments', 0) &gt; 0:
summary += f" ({first_issue.get('comments')} comments)"
issue_info = {
"number": first_issue.get("number"),
"title": first_issue.get("title"),
"user": first_issue.get("user", {}).get("login"),
"url": first_issue.get("html_url"),
"state": first_issue.get("state"),
"comments": first_issue.get("comments"),
"created_at": first_issue.get("created_at"),
"updated_at": first_issue.get("updated_at"),
"body": first_issue.get("body", "")[:500] + "..." if len(first_issue.get("body", "")) &gt; 500 else first_issue.get("body", ""),
"summary": summary
}
print(f"Found first issue: #{first_issue.get('number')} created on {first_issue.get('created_at')}")
# Add context information
result = {
"repository": f"{owner}/{repo_name}",
"note": "This is the very first issue created in this repository",
"first_issue": issue_info
}
return [result]
except httpx.HTTPStatusError as e:
error_message = e.response.json().get("message", "No additional message from API.")
if e.response.status_code == 403 and GITHUB_TOKEN:
error_message += " (Rate limit with token or token lacks permissions?)"
elif e.response.status_code == 403 and not GITHUB_TOKEN:
error_message += " (Rate limit without token. Consider creating a .env file with GITHUB_TOKEN.)"
print(f"GitHub API error: {e.response.status_code}. {error_message}")
return [{
"error": f"GitHub API error: {e.response.status_code}",
"message": error_message
}]
except Exception as e:
print(f"An unexpected error occurred: {str(e)}")
return [{"error": f"An unexpected error occurred: {str(e)}"}]
if __name__ == "__main__":
print("DEBUG: Starting FastMCP GitHub server...")
print(f"DEBUG: Server name: {mcp.name}")
# Initialize and run the server
mcp.run()
Copied

Criamos a tool first_repository_issue, mas não vamos poder usá-la porque ela tem as tags public e first_issue, mas, ao criar o servidor, definimos exclude_tags={"first_issue"}.

Teste de exclude_tagslink image 74

Executamos o cliente MCP

	
< > Input
Python
!cd client_MCP && source .venv/bin/activate && uv run client.py ../gitHub_MCP_server/github_server.py
Copied
>_ Output
			
🔗 Connecting to FastMCP server: ../gitHub_MCP_server/github_server.py
✅ Client created successfully
[06/28/25 10:00:36] INFO Starting MCP server 'GitHubMCP' with ]8;id=28274;file:///Users/macm1/Documents/web/portafolio/posts/client_MCP/.venv/lib/python3.11/site-packages/fastmcp/server/server.py\server.py]8;;\:]8;id=529867;file:///Users/macm1/Documents/web/portafolio/posts/client_MCP/.venv/lib/python3.11/site-packages/fastmcp/server/server.py#1246\1246]8;;\
transport 'stdio'
🛠️ Available tools (1):
==================================================
📋 list_repository_issues
Description: Lists open issues for a given GitHub repository.
Args:
owner: The owner of the repository (e.g., 'modelcontextprotocol')
repo_name: The name of the repository (e.g., 'python-sdk')
Returns:
list[dict]: A list of dictionaries, each containing information about an issue
Parameters: owner, repo_name
🤖 FastMCP client started. Write 'quit', 'q', 'exit', 'salir' to exit.
💬 You can ask questions about GitHub repositories!
📚 The client can use tools from the FastMCP server
------------------------------------------------------------
👤 You:

Vemos que não está disponível a tool first_repository_issue

🛠️  Ferramentas disponíveis (1):
==================================================
📋 listar_issues_do_repositório
Descrição: Lista as issues abertas de um determinado repositório do GitHub.

Argumentos:
owner: O proprietário do repositório (por exemplo, 'modelcontextprotocol')repo_name: O nome do repositório (por exemplo, 'python-sdk')

Retorna:
list[dict]: Uma lista de dicionários, cada um contendo informações sobre um problema
Parâmetros: owner, repo_name

Composição de servidoreslink image 75

Assim como na programação é possível herdar classes, ou construir sobre funções já criadas, no MCP é possível criar sub-servidores e criar uma composição deles.

Servidor MCPlink image 76

Vamos criar um subservidor MCP, com sua própria tool hello_world. Depois, vamos montá-lo no servidor principal. Fazendo isso, vamos poder usar a tool hello_world no cliente que se conectar ao servidor principal.

	
< > Input
Python
%%writefile gitHub_MCP_server/github_server.py
import httpx
from typing import Optional
from fastmcp import FastMCP
from github import GITHUB_TOKEN, create_github_headers
# Create FastMCP server
mcp = FastMCP(
name="GitHubMCP",
instructions="This server provides tools, resources and prompts to interact with the GitHub API.",
include_tags={"public"},
exclude_tags={"first_issue"}
)
sub_mcp = FastMCP(
name="SubMCP",
)
@mcp.tool(tags={"public", "production"})
async def list_repository_issues(owner: str, repo_name: str) -&gt; list[dict]:
"""
Lists open issues for a given GitHub repository.
Args:
owner: The owner of the repository (e.g., 'modelcontextprotocol')
repo_name: The name of the repository (e.g., 'python-sdk')
Returns:
list[dict]: A list of dictionaries, each containing information about an issue
"""
# Limit to first 10 issues to avoid very long responses
api_url = f"https://api.github.com/repos/{owner}/{repo_name}/issues?state=open&amp;per_page=10"
print(f"Fetching issues from {api_url}...")
async with httpx.AsyncClient() as client:
try:
response = await client.get(api_url, headers=create_github_headers())
response.raise_for_status()
issues_data = response.json()
if not issues_data:
print("No open issues found for this repository.")
return [{"message": "No open issues found for this repository."}]
issues_summary = []
for issue in issues_data:
# Create a more concise summary
summary = f"#{issue.get('number', 'N/A')}: {issue.get('title', 'No title')}"
if issue.get('comments', 0) &gt; 0:
summary += f" ({issue.get('comments')} comments)"
issues_summary.append({
"number": issue.get("number"),
"title": issue.get("title"),
"user": issue.get("user", {}).get("login"),
"url": issue.get("html_url"),
"comments": issue.get("comments"),
"summary": summary
})
print(f"Found {len(issues_summary)} open issues.")
# Add context information
result = {
"total_found": len(issues_summary),
"repository": f"{owner}/{repo_name}",
"note": "Showing first 10 open issues" if len(issues_summary) == 10 else f"Showing all {len(issues_summary)} open issues",
"issues": issues_summary
}
return [result]
except httpx.HTTPStatusError as e:
error_message = e.response.json().get("message", "No additional message from API.")
if e.response.status_code == 403 and GITHUB_TOKEN:
error_message += " (Rate limit with token or token lacks permissions?)"
elif e.response.status_code == 403 and not GITHUB_TOKEN:
error_message += " (Rate limit without token. Consider creating a .env file with GITHUB_TOKEN.)"
print(f"GitHub API error: {e.response.status_code}. {error_message}")
return [{
"error": f"GitHub API error: {e.response.status_code}",
"message": error_message
}]
except Exception as e:
print(f"An unexpected error occurred: {str(e)}")
return [{"error": f"An unexpected error occurred: {str(e)}"}]
@mcp.tool(tags={"private", "development"})
async def list_more_repository_issues(owner: str, repo_name: str) -&gt; list[dict]:
"""
Lists open issues for a given GitHub repository.
Args:
owner: The owner of the repository (e.g., 'modelcontextprotocol')
repo_name: The name of the repository (e.g., 'python-sdk')
Returns:
list[dict]: A list of dictionaries, each containing information about an issue
"""
# Limit to first 100 issues to avoid very long responses
api_url = f"https://api.github.com/repos/{owner}/{repo_name}/issues?state=open&amp;per_page=100"
print(f"Fetching issues from {api_url}...")
async with httpx.AsyncClient() as client:
try:
response = await client.get(api_url, headers=create_github_headers())
response.raise_for_status()
issues_data = response.json()
if not issues_data:
print("No open issues found for this repository.")
return [{"message": "No open issues found for this repository."}]
issues_summary = []
for issue in issues_data:
# Create a more concise summary
summary = f"#{issue.get('number', 'N/A')}: {issue.get('title', 'No title')}"
if issue.get('comments', 0) &gt; 0:
summary += f" ({issue.get('comments')} comments)"
issues_summary.append({
"number": issue.get("number"),
"title": issue.get("title"),
"user": issue.get("user", {}).get("login"),
"url": issue.get("html_url"),
"comments": issue.get("comments"),
"summary": summary
})
print(f"Found {len(issues_summary)} open issues.")
# Add context information
result = {
"total_found": len(issues_summary),
"repository": f"{owner}/{repo_name}",
"note": "Showing first 10 open issues" if len(issues_summary) == 10 else f"Showing all {len(issues_summary)} open issues",
"issues": issues_summary
}
return [result]
except httpx.HTTPStatusError as e:
error_message = e.response.json().get("message", "No additional message from API.")
if e.response.status_code == 403 and GITHUB_TOKEN:
error_message += " (Rate limit with token or token lacks permissions?)"
elif e.response.status_code == 403 and not GITHUB_TOKEN:
error_message += " (Rate limit without token. Consider creating a .env file with GITHUB_TOKEN.)"
print(f"GitHub API error: {e.response.status_code}. {error_message}")
return [{
"error": f"GitHub API error: {e.response.status_code}",
"message": error_message
}]
except Exception as e:
print(f"An unexpected error occurred: {str(e)}")
return [{"error": f"An unexpected error occurred: {str(e)}"}]
@mcp.tool(tags={"public", "first_issue"})
async def first_repository_issue(owner: str, repo_name: str) -&gt; list[dict]:
"""
Gets the first issue ever created in a GitHub repository.
Args:
owner: The owner of the repository (e.g., 'modelcontextprotocol')
repo_name: The name of the repository (e.g., 'python-sdk')
Returns:
list[dict]: A list containing information about the first issue created
"""
# Get the first issue by sorting by creation date in ascending order
api_url = f"https://api.github.com/repos/{owner}/{repo_name}/issues?state=all&amp;sort=created&amp;direction=asc&amp;per_page=1"
print(f"Fetching first issue from {api_url}...")
async with httpx.AsyncClient() as client:
try:
response = await client.get(api_url, headers=create_github_headers())
response.raise_for_status()
issues_data = response.json()
if not issues_data:
print("No issues found for this repository.")
return [{"message": "No issues found for this repository."}]
first_issue = issues_data[0]
# Create a detailed summary of the first issue
summary = f"#{first_issue.get('number', 'N/A')}: {first_issue.get('title', 'No title')}"
if first_issue.get('comments', 0) &gt; 0:
summary += f" ({first_issue.get('comments')} comments)"
issue_info = {
"number": first_issue.get("number"),
"title": first_issue.get("title"),
"user": first_issue.get("user", {}).get("login"),
"url": first_issue.get("html_url"),
"state": first_issue.get("state"),
"comments": first_issue.get("comments"),
"created_at": first_issue.get("created_at"),
"updated_at": first_issue.get("updated_at"),
"body": first_issue.get("body", "")[:500] + "..." if len(first_issue.get("body", "")) &gt; 500 else first_issue.get("body", ""),
"summary": summary
}
print(f"Found first issue: #{first_issue.get('number')} created on {first_issue.get('created_at')}")
# Add context information
result = {
"repository": f"{owner}/{repo_name}",
"note": "This is the very first issue created in this repository",
"first_issue": issue_info
}
return [result]
except httpx.HTTPStatusError as e:
error_message = e.response.json().get("message", "No additional message from API.")
if e.response.status_code == 403 and GITHUB_TOKEN:
error_message += " (Rate limit with token or token lacks permissions?)"
elif e.response.status_code == 403 and not GITHUB_TOKEN:
error_message += " (Rate limit without token. Consider creating a .env file with GITHUB_TOKEN.)"
print(f"GitHub API error: {e.response.status_code}. {error_message}")
return [{
"error": f"GitHub API error: {e.response.status_code}",
"message": error_message
}]
except Exception as e:
print(f"An unexpected error occurred: {str(e)}")
return [{"error": f"An unexpected error occurred: {str(e)}"}]
@sub_mcp.tool(tags={"public"})
def hello_world() -&gt; str:
"""
Returns a simple greeting.
"""
return "Hello, world!"
mcp.mount("sub_mcp", sub_mcp)
if __name__ == "__main__":
print("DEBUG: Starting FastMCP GitHub server...")
print(f"DEBUG: Server name: {mcp.name}")
# Initialize and run the server
mcp.run()
Copied
>_ Output
			
Overwriting gitHub_MCP_server/github_server.py

Teste da composição de servidores MCPlink image 77

Executamos o cliente

	
< > Input
Python
!cd client_MCP && source .venv/bin/activate && uv run client.py ../gitHub_MCP_server/github_server.py
Copied
>_ Output
			
🔗 Connecting to FastMCP server: ../gitHub_MCP_server/github_server.py
✅ Client created successfully
/Users/macm1/Documents/web/portafolio/posts/gitHub_MCP_server/github_server.py:240: DeprecationWarning: Mount prefixes are now optional and the first positional argument should be the server you want to mount.
mcp.mount("sub_mcp", sub_mcp)
[06/28/25 10:10:58] INFO Starting MCP server 'GitHubMCP' with transport 'stdio' server.py:1246
🛠️ Available tools (2):
==================================================
📋 sub_mcp_hello_world
Description: Returns a simple greeting.
Parameters:
📋 list_repository_issues
Description: Lists open issues for a given GitHub repository.
Args:
owner: The owner of the repository (e.g., 'modelcontextprotocol')
repo_name: The name of the repository (e.g., 'python-sdk')
Returns:
list[dict]: A list of dictionaries, each containing information about an issue
Parameters: owner, repo_name
🤖 FastMCP client started. Write 'quit', 'q', 'exit', 'salir' to exit.
💬 You can ask questions about GitHub repositories!
📚 The client can use tools from the FastMCP server
------------------------------------------------------------
👤 You: Can you greeting me?
🤔 Claude is thinking...
🔧 Claude wants to use: sub_mcp_hello_world
📝 Arguments: {}
✅ Tool executed successfully
🤖 Claude: I'll help you send a greeting using the `sub_mcp_hello_world` function. This function returns a simple greeting.There's your greeting! The function returned "Hello, world!"
👤 You: q
👋 Bye!
🧹 Cleaning up resources...
✅ Resources released

Podemos ver que apareceu a nova tool sub_mcp_hello_world

🛠️  Ferramentas disponíveis (2):
==================================================
📋 sub_mcp_hello_world
Descrição: Retorna uma saudação simples.
Parâmetros:

📋 listar_issues_do_repositório
Descrição: Lista os issues abertos para um determinado repositório do GitHub.

Args:
owner: O proprietário do repositório (por exemplo, 'modelcontextprotocol')
repo_name: O nome do repositório (por exemplo, 'python-sdk')

Retorna:
list[dict]: Uma lista de dicionários, cada um contendo informações sobre um problema
Parâmetros: owner, repo_name

E quando pedimos para que nos cumprimente, ele executa.

👤 Você: Pode me cumprimentar?

🤔 Claude está pensando...
🔧 Claude quer usar: sub_mcp_hello_world
📝 Argumentos: {}✅ Ferramenta executada com sucesso

🤖 Claude: Vou ajudá-lo a enviar uma saudação usando a função `sub_mcp_hello_world`. Esta função retorna uma saudação simples. Aí está a sua saudação! A função retornou "Hello, world!"

---

➡️ **Continua na Parte 2: camada de transporte, contexto e resources**.

Continuar lendo

Últimos posts -->

Você viu esses projetos?

Gymnasia

Gymnasia Gymnasia
React Native
Expo
TypeScript
FastAPI
Next.js
OpenAI
Anthropic

Aplicativo móvel de treino pessoal com assistente de IA, biblioteca de exercícios, acompanhamento de rotinas, dieta e medidas corporais

Horeca chatbot

Horeca chatbot Horeca chatbot
Python
LangChain
PostgreSQL
PGVector
React
Kubernetes
Docker
GitHub Actions

Chatbot conversacional para cozinheiros de hotéis e restaurantes. Um cozinheiro, gerente de cozinha ou serviço de quarto de um hotel ou restaurante pode falar com o chatbot para obter informações sobre receitas e menus. Mas também implementa agentes, com os quais pode editar ou criar novas receitas ou menus

Naviground

Naviground Naviground
Ver todos os projetos -->
>_ Disponível para projetos

Tem um projeto com IA?

Vamos conversar.

maximofn@gmail.com

Especialista em Machine Learning e Inteligência Artificial. Desenvolvo soluções com IA generativa, agentes inteligentes e modelos personalizados.

Quer assistir alguma palestra?

Últimas palestras -->

Quer melhorar com essas dicas?

Últimos tips -->

Use isso localmente

Os espaços do Hugging Face nos permitem executar modelos com demos muito simples, mas e se a demo quebrar? Ou se o usuário a deletar? Por isso, criei contêineres docker com alguns espaços interessantes, para poder usá-los localmente, aconteça o que acontecer. Na verdade, se você clicar em qualquer botão de visualização de projeto, ele pode levá-lo a um espaço que não funciona.

Flow edit

Flow edit Flow edit

Edite imagens com este modelo de Flow. Baseado em SD3 ou FLUX, você pode editar qualquer imagem e gerar novas

FLUX.1-RealismLora

FLUX.1-RealismLora FLUX.1-RealismLora
Ver todos os contêineres -->
>_ Disponível para projetos

Tem um projeto com IA?

Vamos conversar.

maximofn@gmail.com

Especialista em Machine Learning e Inteligência Artificial. Desenvolvo soluções com IA generativa, agentes inteligentes e modelos personalizados.

Você quer treinar seu modelo com esses datasets?

short-jokes-dataset

HuggingFace

Dataset com piadas em inglês

Uso: Fine-tuning de modelos de geração de texto humorístico

231K linhas 2 colunas 45 MB
Ver no HuggingFace →

opus100

HuggingFace

Dataset com traduções de inglês para espanhol

Uso: Treinamento de modelos de tradução inglês-espanhol

1M linhas 2 colunas 210 MB
Ver no HuggingFace →

netflix_titles

HuggingFace

Dataset com filmes e séries da Netflix

Uso: Análise de catálogo Netflix e sistemas de recomendação

8.8K linhas 12 colunas 3.5 MB
Ver no HuggingFace →
Ver mais datasets -->