servers VS fastmcp

Most agents don't speak raw HTTP — they speak MCP. So the real distribution surface is a tiny MCP server that exposes each endpoint as a tool and does the paying under the hood. The agent calls web_search(query); the server hits the paid endpoint, handles the 402 with its operator's wallet, returns JSON. One line to install:

The Model Context Protocol (MCP) is an open standard for connecting AI hosts (Claude, ChatGPT, Cursor, VS Code, and others) to external context and actions through a structured protocol instead of ad-hoc plugins.

Model Context Protocol specification

That changed when I built mpesa-mcp — a Model Context Protocol server that wraps the Daraja API and makes it directly available to Claude, GPT-4, and Gemini.

If you haven't run into it yet, MCP (Model Context Protocol) is an open standard for exposing tools to AI agents. two-go ships an MCP server that runs over stdio with no dependencies, no URL, no account, and no API key. It's all local.

MCP server surface. The CLI is already agent-friendly, so I'm double minded about whether a Model Context Protocol server would make integration smoother. I'm personally not a fan of MCP and prefer skills and CLIs.

As noted in the Model Context Protocol (MCP) Introduction, this protocol allows Claude to interact with systems like GitHub and local databases through a standardized interface.

Keep the project's living context: current task state, decisions, conventions, known footguns as plain Markdown committed to the repo, and expose it to the agent over MCP.

The MCP ecosystem doubles every few months. Bookmark the official MCP servers repository for the canonical list.

The first version was deliberately simple. As I described in this post my team and I had very limited knowledge of MCP servers and add to discover the topic, and learn it while (vibe)coding the Proof of Concept. It turned out that creating an MCP server with STDIO transport is really trivial. Especially thanks to FastMCP: in the end it's about wrapping the desired endpoints of our API with the @mcp.tool() annotation.

The official MCP specification and FastMCP docs cover advanced patterns: resources, prompts, progress reporting, and OAuth authentication — all worth exploring once your first server is running.

╭──────────────────────────────────────────────────────────────────────────────╮ │ │ │ │ │ ▄▀▀ ▄▀█ █▀▀ ▀█▀ █▀▄▀█ █▀▀ █▀█ │ │ █▀ █▀█ ▄▄█ █ █ ▀ █ █▄▄ █▀▀ │ │ │ │ │ │ FastMCP 3.2.0 │ │ https://gofastmcp.com │ │ │ │ 🖥 Server: hello-world-server, 3.2.0 │ │ 🚀 Deploy free: https://horizon.prefect.io │ │ │ ╰──────────────────────────────────────────────────────────────────────────────╯ [04/04/26 19:10:50] INFO Starting MCP server 'hello-world-server' with transport 'http' on transport.py:299 http://0.0.0.0:8080/mcp INFO: Started server process [27502] INFO: Waiting for application startup. {"message": "StreamableHTTP session manager started"} INFO: Application startup complete. INFO: Uvicorn running on http://0.0.0.0:8080 (Press CTRL+C to quit)

https://github.com/PrefectHQ/fastmcp/pull/54

It was incorporated on Dec 21 2024, and hardened through the efforts of one of the python-sdk maintainers.

The author seemingly abandoned the github project for several months and marked it as unmaintained:

The full FastMCP docs are at gofastmcp.com. The Tools guide covers advanced patterns like async tools, Pydantic validation, and custom error handling.

We’ll use a library called FastMCP that simplifies the process of building MCP servers. You can install it using pip:

- FastMCP backend (https://github.com/jlowin/fastmcp) with manual upload to AWS S3. On-prem/air-gapped/local/your-cloud options coming. Zero training on uploaded docs, even in beta.

GitHub