py2mcp
Quick MCP (Model Context Protocol) server creation from Python functions.
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).
License
MIT
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