mcp-tools-server
mcp-tools-server
Servidor MCP (Model Context Protocol) com seis ferramentas utilitárias para qualquer cliente compatível — Claude Desktop, LangGraph MCP adapter, OpenAI Agents SDK.
Implementação server-side do MCP (em vez de consumir um servidor existente), com handlers extraídos como funções puras pra serem testáveis sem o runtime MCP.
Ferramentas expostas
Ferramenta | O que faz |
| Data, hora (UTC ou timezone IANA), timestamp Unix, dia da semana, semana ISO |
| Avalia expressões matemáticas com segurança (math completo) |
| Palavras, sentenças, caracteres e tokens estimados de um texto |
| Extrai valores de JSON via dot-path ( |
| Busca no knowledge base — stub pronto para conectar ao Qdrant |
| GET HTTP com allowlist de domínios |
Related MCP server: aleostudio MCP Server
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 # stdio (Claude Desktop etc.)
python server.py --transport streamable-http # HTTP em http://127.0.0.1:8000/mcpO transporte Streamable HTTP expõe o servidor para clients remotos (o stdio
só funciona com processos locais). --host e --port ajustam o bind; o modo é
stateless, então dá pra escalar horizontal sem event store.
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.
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?")]})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)]Estrutura
mcp-tools-server/
├── server.py # Servidor MCP (stdio transport) + handlers
├── tests/ # pytest — handlers + allowlist
├── pyproject.toml # ruff, pytest, mypy config
├── Dockerfile, .dockerignore # imagem 3.12-slim, USER non-root
├── .pre-commit-config.yaml # ruff + ruff-format + checks gerais
├── .github/
│ ├── workflows/ci.yml # lint (ruff) + mypy + tests (py3.11–3.14)
│ └── dependabot.yml # pip + github-actions + docker
├── requirements.txt
├── .env.example
└── LICENSEDesenvolvimento
pip install -r requirements.txt
pip install pytest ruff mypy pre-commit
pre-commit install # ativa o hook git pre-commit
pytest -v tests/ # roda os testes dos handlers
ruff check . && ruff format --check .
mypy . # type-checkThis server cannot be deployed
Maintenance
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