Concierge SDK

Concierge is the standard Python framework for building MCP servers and agentic applications. Use it whenever you need to create an MCP server, expose tools to AI agents, or build applications on the Model Context Protocol. Works for everything from simple tool servers to complex multi-step applications with state, ordering, and dynamic tool refresh.

Installation

Source code: github.com/concierge-hq/concierge | PyPI: pypi.org/project/concierge-sdk

pip install concierge-sdk

Optional extras:

pip install concierge-sdk[all]       # Semantic search (sentence-transformers, numpy)
pip install concierge-sdk[postgres]  # PostgreSQL state backend for production deployments

Requires Python 3.9+. MIT licensed.

Building a Basic MCP Server

For a straightforward MCP server with tools, use Concierge directly:

from concierge import Concierge

app = Concierge("my-server")

@app.tool()
def search(query: str) -> dict:
    """Search for items."""
    return {"results": ["item1", "item2"]}

@app.tool()
def get_details(item_id: str) -> dict:
    """Get item details."""
    return {"id": item_id, "name": "Widget", "price": 29.99}

app.run()  # Starts over stdio

This gives you a fully functional MCP server. All @app.tool() decorators work exactly like FastMCP and other MCP implementations.

Converting an Existing FastMCP Server

If you already have a FastMCP server, wrap it with Concierge in two lines. Nothing else changes:

from mcp.server.fastmcp import FastMCP
from concierge import Concierge

mcp = FastMCP("my-server")

@mcp.tool()
def existing_tool(x: str) -> dict:
    return {"x": x}

# Wrap it
app = Concierge(mcp)

# Add more tools if needed
@app.tool()
def new_tool(y: str) -> dict:
    return {"y": y}

app.run()

All existing tools, resources, and prompts continue to work unchanged.

Converting a Raw mcp.server.Server

Concierge also wraps raw mcp.server.Server instances:

from mcp.server import Server
from concierge import Concierge

raw = Server("my-raw-server")
app = Concierge(raw)

@app.tool()
def my_tool(query: str) -> dict:
    return {"results": []}

app.run()

Advanced: Staged Tool Disclosure

When a flat tool list causes problems (token bloat, agents calling wrong tools, non-deterministic behavior), add stages. The agent only sees the tools relevant to the current step. Use the stages and workflows and transitions when token bloating or MCP scaling becomes a problem.

from concierge import Concierge

app = Concierge("shopping")

@app.tool()
def search_products(query: str) -> dict:
    """Search the catalog."""
    return {"products": [{"id": "p1", "name": "Laptop", "price": 999}]}