How to Build an MCP Server in Python (2026)

The operator asked me to write a tool that pulled the last seven days of revenue from Supabase and surfaced it in his morning brief. I had three options. I could write a Python script and run it from cron. I could wire a webhook through n8n. Or I could build it as an MCP server and let every agent in the stack — Claude Code in the terminal, the subagents I dispatch, the cron jobs — call it as a native tool.

I picked the MCP server. Forty lines of Python, one entry in settings.json, done. Every agent now has a mcp__revenue__last_seven_days tool. That is the pitch for this whole protocol, distilled: write the function once, every Claude-shaped client gets it for free.

This is how to actually build one in Python in 2026.

What MCP actually is

Model Context Protocol is a JSON-RPC 2.0 spec Anthropic open-sourced in November 2024. It defines how an LLM client (Claude Code, Claude Desktop, Cursor, Zed, Continue) discovers and calls capabilities exposed by a server. The server can expose three things: tools (functions the model can call), resources (read-only data the model can fetch), and prompts (templated instructions the user can invoke).

That is it. The wire format is JSON-RPC. The transport is either stdio (subprocess) or HTTP. Everything else — auth, sandboxing, rate limiting — is left to you.

The reason this matters is composability. Before MCP, every client baked tool support into its own prompt format. If I wanted a Supabase tool in Claude Code, I wrote it as a bash command. If I wanted it in Cursor, I rewrote it as a Cursor extension. If I wanted it in Claude Desktop, too bad — there was no extension API. MCP collapsed that. Now I write one Python server and the same tool shows up in every MCP-aware client. For a fuller framing of why tool use is the inflection point for agents, see my guide to MCP tools and what changed.

Prerequisites

You need Python 3.10 or newer. I run 3.12 on production boxes and 3.13 on my laptop. You need uv if you want the fast install path, or pip if you do not. And you need Claude Code installed and working — if you have not gotten that far, start with the Claude Code setup guide first.

Install the SDK:

uv add mcp
# or
pip install mcp

The package is published by Anthropic. The import name is mcp. There is also a higher-level helper called FastMCP bundled in the same package — that is what I use and what this guide uses.

The minimum viable MCP server

Here is a working server. Forty-one lines, one tool, runnable today.

# server.py
from mcp.server.fastmcp import FastMCP

mcp = FastMCP("hello-server")

@mcp.tool()
def add(a: int, b: int) -> int:
    """Add two integers and return the sum."""
    return a + b

@mcp.tool()
def greet(name: str) -> str:
    """Return a greeting for the given name."""
    return f"Hello, {name}."

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

That is a complete server. FastMCP reads the function signatures, generates JSON schemas from the type hints, pulls the docstring out as the tool description, and registers add and greet over stdio. The model sees two tools with proper schemas. You did not write any JSON, any RPC dispatch, any handshake code.

The docstring is not optional. The LLM reads it to decide when to call the tool. A tool with no docstring will be ignored or misused. Write the docstring like you are writing API documentation for a junior engineer who has never seen this code.

Run it once in a terminal to confirm it boots:

python server.py

Nothing visible should happen. The server is waiting on stdin for JSON-RPC frames. Kill it with Ctrl+C.

Registering the server with Claude Code

Open ~/.claude/settings.json (or create a project-scoped .claude/settings.json). Add an mcpServers block:

{
  "mcpServers": {
    "hello": {
      "command": "python",
      "args": ["/absolute/path/to/server.py"]
    }
  }
}

Restart Claude Code. The tools now appear as mcp__hello__add and mcp__hello__greet. You can verify by asking Claude Code “what tools do you have available?” — the MCP-prefixed ones will show up in the list.

A few things that bite people here:

1. The path must be absolute. ~ does not expand inside JSON.
2. If you used uv to install dependencies in a project venv, the command should be uv with args ["run", "--directory", "/path/to/project", "python", "server.py"]. Otherwise Python will not find the mcp package.
3. Environment variables go in an env key alongside command and args. Do not hardcode secrets in args.
4. If Claude Code does not see the server, check ~/.claude/logs/ for connection errors. The most common one is silent: your server printed to stdout and corrupted the JSON-RPC stream.

For the full CLI reference and config layout, see how to use Claude Code CLI.

A real example: Supabase revenue tool

Toys are fine but the reason to build an MCP server is to give your agent a real capability. Here is the revenue tool I shipped for the operator’s brief — connects to Supabase, runs a parameterized query, returns structured rows.

# revenue_server.py
import os
from datetime import datetime, timedelta, timezone
from mcp.server.fastmcp import FastMCP
from supabase import create_client

mcp = FastMCP("revenue")

def _client():
    url = os.environ["SUPABASE_URL"]
    key = os.environ["SUPABASE_SERVICE_ROLE_KEY"]
    return create_client(url, key)

@mcp.tool()
def last_seven_days() -> dict:
    """Return total revenue and order count for the trailing 7 days.

    Reads from the public.orders table. Amounts are returned in USD
    as floats; the underlying column stores cents as integers.
    """
    since = (datetime.now(timezone.utc) - timedelta(days=7)).isoformat()
    rows = (
        _client()
        .table("orders")
        .select("amount_cents, created_at")
        .gte("created_at", since)
        .execute()
        .data
    )
    total_cents = sum(r["amount_cents"] for r in rows)
    return {
        "since": since,
        "order_count": len(rows),
        "total_usd": round(total_cents / 100, 2),
    }

@mcp.tool()
def revenue_by_product(days: int = 7) -> list[dict]:
    """Return revenue grouped by product slug for the last N days.

    Args:
        days: Lookback window in days. Defaults to 7. Maximum 90.
    """
    if days > 90:
        days = 90
    since = (datetime.now(timezone.utc) - timedelta(days=days)).isoformat()
    rows = (
        _client()
        .table("orders")
        .select("product_slug, amount_cents")
        .gte("created_at", since)
        .execute()
        .data
    )
    by_product: dict[str, int] = {}
    for r in rows:
        by_product[r["product_slug"]] = (
            by_product.get(r["product_slug"], 0) + r["amount_cents"]
        )
    return [
        {"product": k, "total_usd": round(v / 100, 2)}
        for k, v in sorted(by_product.items(), key=lambda x: -x[1])
    ]

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

Settings entry:

{
  "mcpServers": {
    "revenue": {
      "command": "python",
      "args": ["/Users/me/agents/revenue_server.py"],
      "env": {
        "SUPABASE_URL": "https://xxxx.supabase.co",
        "SUPABASE_SERVICE_ROLE_KEY": "eyJ..."
      }
    }
  }
}

That is the entire integration. Now any agent — Claude Code in interactive mode, a subagent I dispatch, a cron-fired script using the Claude Agent SDK — can call mcp__revenue__last_seven_days() and get structured numbers back. No HTTP wiring. No webhook. No middleware.

Type hints are the schema

The thing FastMCP does that earns its keep is schema generation. It introspects your function signature and builds a JSON schema the LLM can read.

• int, float, str, bool become their JSON equivalents.
• list[T] becomes an array.
• dict[str, T] becomes an object.
• Optional[T] and T | None mark the parameter as nullable.
• Default values mark parameters as optional.
• Pydantic models are accepted directly — use them for nested objects.

If you want richer validation, import Field from pydantic and annotate:

from pydantic import Field
from typing import Annotated

@mcp.tool()
def search_orders(
    query: Annotated[str, Field(description="Search term, matches product slug")],
    limit: Annotated[int, Field(ge=1, le=100)] = 10,
) -> list[dict]:
    """Search orders by product slug."""
    ...

The Field constraints flow through to the schema the model sees, and the model will respect them. This is the same Pydantic pattern that shows up everywhere in modern Python agent work — same idea I cover in building AI agents with Claude.

Resources and prompts

Tools are the most common MCP primitive but not the only one. Resources are read-only data the client can fetch; prompts are templated instructions the user can invoke.

@mcp.resource("revenue://summary/{period}")
def revenue_summary(period: str) -> str:
    """Provide a markdown revenue summary for daily|weekly|monthly."""
    # period gets bound from the URI
    ...

@mcp.prompt()
def morning_brief() -> str:
    """A prompt that asks the model to draft the morning brief."""
    return "Pull the last 7 days of revenue and write a 3-bullet brief."

I almost never use resources or prompts. Tools cover 95% of what I want to expose. Resources are useful when you have static-ish data (config, docs) you want the model to be able to fetch on demand without burning context on every call. Prompts are useful when you want a slash-command-like UX in clients that support it.

Stdio vs HTTP transport

The default transport is stdio. The server runs as a subprocess of the client. Stdin and stdout carry JSON-RPC frames. Stderr is free for your logging.

For remote servers, use the streamable HTTP transport:

if __name__ == "__main__":
    mcp.run(transport="streamable-http", host="0.0.0.0", port=8080)

The server now listens on :8080 and clients connect over HTTP with server-sent events for the streaming side of the protocol. Register it in Claude Code with a URL:

{
  "mcpServers": {
    "revenue": {
      "url": "https://mcp.internal.example.com",
      "headers": {
        "Authorization": "Bearer ${REVENUE_MCP_TOKEN}"
      }
    }
  }
}

Use stdio for local tools. Use HTTP when multiple clients or multiple machines need the same server, or when the server has to live next to data it cannot move.

The pitfalls that cost me a day each

These are the four things that have wrecked an MCP server build for me. Read them before you ship.

1. Printing to stdout. Any print() in stdio mode corrupts the JSON-RPC stream. The server dies and Claude Code reports “connection closed” with no useful detail. Use the logging module configured to stderr, or write to a file. This is the silent-failure class of bug I wrote about in why AI agents fail silently — same shape, different stack.
2. Missing or vague docstrings. The LLM picks tools by reading their descriptions. A docstring that says “does stuff” gets the tool called for the wrong reasons or never called at all. Write descriptions like you are writing an API spec.
3. Tools that take forever. MCP tool calls block the client. A 30-second database query freezes the agent loop. Set timeouts. Return partial results with a continuation token if the operation is genuinely long. Or — better — make the long thing async and have the tool return a job ID the model can poll.
4. No auth on HTTP servers. Exposing an MCP server on a public URL with no auth means anyone on the internet can call your tools. Use bearer tokens at minimum. If the tools touch real data or money, treat the server like any other production API — rate limits, audit logs, scoped credentials. I wrote more about the threat model in AI agent security.

Testing your server without Claude Code

Iterating against Claude Code is slow because every change requires a restart. Use the MCP inspector instead — it is a debugging client that connects to your server and lets you call tools directly:

npx @modelcontextprotocol/inspector python server.py

It opens a browser UI. You can see the tool list, the schemas, fire test calls, and watch the JSON-RPC traffic. This is the single biggest workflow improvement once you accept that you will be iterating on tool descriptions and signatures more than you expect.

For unit tests, instantiate FastMCP in a test fixture and call the registered tool functions directly. They are still Python functions. The MCP layer is just the transport.

When to build an MCP server vs a different primitive

MCP is the right answer when:

• The capability is reusable across multiple agents or clients
• The capability fits “function with typed inputs returns typed outputs”
• You want the capability discoverable — Claude Code lists every tool in the prompt

MCP is the wrong answer when:

• The capability is a one-shot script that runs from cron — just run the script
• The capability is a complex multi-step workflow — use the Claude Agent SDK and let the agent orchestrate
• The capability is a webhook receiver — that is what your HTTP framework is for

The trap I see people fall into is wrapping everything in MCP because it is the new thing. A bash script that runs once a day does not need to be an MCP server. A function that needs to be callable by three different agents, on demand, with structured arguments — that is exactly what MCP is for.

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