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

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

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-4-6"), tools)
result = await agent.ainvoke({"messages": [("human", "What day of the week is it?")]})

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)]

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