Tools Server

Servidor MCP (Model Context Protocol) de propósito geral com ferramentas utilitárias prontas para consumo por qualquer agente compatível.

Demonstra implementação server-side do MCP — a maioria dos projetos apenas consome servidores. Este projeto implementa um.

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Ferramentas expostas

Ferramenta	O que faz
datetime_info	Data, hora UTC, timestamp Unix, dia da semana, semana ISO
calculate	Avalia expressões matemáticas com segurança (math completo)
text_stats	Palavras, sentenças, caracteres e tokens estimados de um texto
json_extract	Extrai valores de JSON via dot-path (user.address.city)
search_knowledge	Busca no knowledge base — stub pronto para conectar ao Qdrant
http_get	GET HTTP com allowlist de domínios

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Quick start

git clone https://github.com/RenanMiqueloti/mcp-tools-server.git
cd mcp-tools-server
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
python server.py

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Conectar ao Claude Desktop

Adicione em ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) ou %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "mcp-tools": {
      "command": "python",
      "args": ["/caminho/absoluto/para/server.py"]
    }
  }
}

Reinicie o Claude Desktop. As ferramentas ficam disponíveis automaticamente.

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Conectar a um agente LangGraph

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_anthropic import ChatAnthropic

client = MultiServerMCPClient({
    "mcp-tools": {
        "command": "python",
        "args": ["server.py"],
        "transport": "stdio",
    }
})

tools = await client.get_tools()
agent = create_react_agent(ChatAnthropic(model="claude-opus-4-7"), tools)
result = await agent.ainvoke({"messages": [("human", "What day of the week is it?")]})

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Adicionar o search_knowledge real (Qdrant)

Em server.py, substitua o stub no handler search_knowledge:

from qdrant_client import QdrantClient
from langchain_openai import OpenAIEmbeddings

client_q = QdrantClient(url=os.getenv("QDRANT_URL"))
embeddings = OpenAIEmbeddings()

query_vec = embeddings.embed_query(query)
hits = client_q.search("knowledge", query_vector=query_vec, limit=top_k)
results = [{"rank": i+1, "text": h.payload["text"], "score": h.score} for i, h in enumerate(hits)]

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Estrutura

mcp-tools-server/
├── server.py         # Servidor MCP completo (stdio transport)
├── requirements.txt
├── .env.example
└── LICENSE