py2mcp

Quick MCP (Model Context Protocol) server creation from Python functions.

For AI agents

py2mcp publishes its documentation in forms made for coding agents. If you are one, start here.

The documentation, machine-readable: llms.txt indexes every page; py2mcp.md is the whole documentation in one file; every page has a .md twin; objects.inv maps symbols to URLs.

If you identify as a dinosaur, the rest of this README is written for you, starting at Installation.

Installation

pip install py2mcp

Quick Start

from py2mcp import mk_mcp_server


def add(a: int, b: int) -> int:
    """Add two numbers"""
    return a + b


def greet(name: str = "world") -> str:
    """Greet someone"""
    return f"Hello, {name}!"


# Create and run MCP server
mcp = mk_mcp_server([add, greet])

if __name__ == "__main__":
    mcp.run()

That's it! Your functions are now available as MCP tools.

Features

• Simple: Just pass functions to mk_mcp_server()
• Flexible: Supports input/output transformations
• Pythonic: Clean, decorator-free function definitions
• Powerful: Built on FastMCP for production-ready servers

Input Transformations

Transform inputs before they reach your functions:

from py2mcp import mk_mcp_server, mk_input_trans
import numpy as np


def add_arrays(a, b):
    """Add two numpy arrays"""
    return (a + b).tolist()


# Convert list inputs to numpy arrays
input_trans = mk_input_trans({"a": np.array, "b": np.array})
mcp = mk_mcp_server([add_arrays], input_trans=input_trans)

From Stores (MutableMapping)

Automatically expose CRUD operations from any mapping:

from py2mcp import mk_mcp_from_store

projects = {"proj1": {"name": "Project 1"}, "proj2": {"name": "Project 2"}}
mcp = mk_mcp_from_store(projects, name="project")

# Automatically creates: list_projects, get_project, set_project, delete_project

Serving: local (stdio) and remote (HTTP + OAuth)

mk_mcp_* build a server object; py2mcp also gives you two ways to run one.

Local (stdio) — for a one-click bundle (e.g. a Claude Desktop .mcpb):

from py2mcp import serve_stdio

serve_stdio(["mypkg.tools:summarize", "mypkg.tools:translate"], name="My Tools")
# or:  python -m py2mcp --config py2mcp_config.json

Remote (Streamable HTTP + OAuth 2.1) — for a hosted MCP server reached from a vendor's cloud (e.g. a claude.ai custom connector). The server is an OAuth 2.1 resource server: it validates a managed IdP's JWTs (audience-bound per RFC 8707) and never issues tokens itself.

from py2mcp.http import mk_http_app

AUTH = {
    "type": "jwt",  # resource-server: validate the IdP's JWTs
    "jwks_uri": "https://idp.example.com/.well-known/jwks.json",
    "issuer": "https://idp.example.com",
    "audience": "https://my-connector.example.com/mcp",  # THIS server (RFC 8707)
    "authorization_servers": ["https://idp.example.com"],
    "base_url": "https://my-connector.example.com",
    "required_scopes": ["mcp:read"],
}

# An ASGI app you run under any ASGI server (uvicorn, gunicorn, serverless):
app = mk_http_app(["mypkg.tools:summarize"], name="My Connector", auth=AUTH)
#   uvicorn server.app:app --host 0.0.0.0 --port 8000   (behind TLS)

serve_http(...) builds and runs it in-process (FastMCP/uvicorn). Both wrap FastMCP's native transports/OAuth — py2mcp does not reinvent them.

Middleware (metering, logging, rate-limiting)

Every builder accepts middleware= — a single FastMCP middleware or an iterable of them — attached at construction, exactly as auth= is. It's the one clean seam for cross-cutting concerns that must wrap every tool call (usage metering, cost logging, audit trails, rate limiting), so you don't decorate each function individually — and can't forget one (a missed decorator on a paid tool means untracked cost):

from fastmcp.server.middleware import Middleware


class UsageMeter(Middleware):
    async def on_call_tool(self, context, call_next):
        result = await call_next(context)  # the tool runs here
        record(context.message.name)  # ... then meter it
        return result


mcp = mk_mcp_server([render, estimate], middleware=[UsageMeter()])
# same on mk_mcp_from_refs(...), mk_mcp_from_store(...), mk_http_app(...),
#         serve_http(...), serve_stdio(...)

On the remote path auth= (transport-level) runs first, so a middleware can read the authenticated caller via fastmcp.server.dependencies.get_access_token(). Middleware is a programmatic hook — it takes Python objects, so it isn't wired through the python -m py2mcp CLI / JSON-config path (unlike refs/name/auth).

Instructions (the server's model-facing description)

Every builder also accepts instructions= — a natural-language string surfaced to the connecting client/model as the server's instructions, attached at construction exactly like auth=/middleware=. It's the place to say what the tools are for and the intended workflow, so a model can orient itself without calling a tool:

mcp = mk_mcp_server(
    [render, estimate],
    instructions="Turn source docs into narrated audio. Always estimate_cost before a render.",
)
# same keyword on mk_mcp_from_refs(...), mk_mcp_from_store(...), mk_http_app(...),
#                 serve_http(...), serve_stdio(...)

Like middleware=, it's a programmatic argument (not yet wired through the python -m py2mcp CLI / JSON-config path).

License

MIT