How to Build a Custom MCP Server with Gitingest, FastMCP & Gemini 2.5 Pro

Why Roll Your Own MCP Server?

• Custom tool endpoints tailored to your codebase or APIs
• Minimal boilerplate thanks to FastMCP’s decorators
• Automated scaffolding with Gemini 2.5 Pro in AI Studio
• Rapid iteration: from repo to running server in minutes

Prerequisites

1. Python 3.10+ (with access to pipx or a virtual environment)
2. Git (to clone repos)
3. AI Studio account with access to Gemini 2.5 Pro
4. uv CLI (installed via pip install uv for local testing)

1. Install & Run Gitingest

Gitingest turns any GitHub repository into a single prompt‑friendly text file.

# Install via pipx for isolation
pipx install gitingest

# Ingest the FastMCP repo
gitingest https://github.com/jlowin/fastmcp -o fastmcp_source.txt

• Output: fastmcp_source.txt contains directory structure, READMEs, and source files.

2. Write Your MCP Server (server.py)

Using FastMCP v2.x, define your tools in pure Python. Here’s a minimal example exposing a “directory tree” endpoint:

# server.py
# requirements:
#   fastmcp>=2.0
#   httpx>=0.27
#   rich>=13.7

from fastmcp import FastMCP
import httpx
from rich.tree import Tree

# Initialize the MCP server instance
mcp = FastMCP("GitHub Directory Server")

@mcp.tool()
async def github_directory_structure(
    owner: str,
    repo: str,
    branch: str = "main",
) -> str:
    """
    Return the repo’s directory tree in ASCII form.
    """
    url = f"https://api.github.com/repos/{owner}/{repo}/git/trees/{branch}?recursive=1"
    async with httpx.AsyncClient() as client:
        resp = await client.get(url, timeout=10)
        resp.raise_for_status()
        data = resp.json()

    tree = Tree(f"{owner}/{repo}@{branch}")
    for entry in data["tree"]:
        if entry["type"] == "tree":
            tree.add(entry["path"])
    return tree.__str__()

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

Key points:

• Use @mcp.tool() (with parentheses) to register each endpoint.
• Return primitives (str, list, etc.)—FastMCP handles JSON serialization.
• Async I/O via httpx.AsyncClient() keeps your server non‑blocking.

3. Manage Dependencies & Run Locally

Option A: Inline PEP 723 Block

Simply list your requirements at the top of server.py (shown above), then:

pip install uv
uv run server.py

Option B: Dedicated requirements.txt

fastmcp>=2.0
httpx>=0.27
rich>=13.7

pip install -r requirements.txt uv
uv run server.py

For a richer local dev experience (hot‑reload, inspector UI):

uv init my-mcp
cd my-mcp
uv venv && source .venv/bin/activate
uv pip install fastmcp httpx rich
fastmcp dev ../server.py

4. Test Your Endpoint

In another terminal, invoke the tool via MCP’s CLI:

uv run mcp call mcp github_directory_structure \
  --json '{"owner":"jlowin","repo":"fastmcp"}'

You should see an ASCII tree of the fastmcp repo.

5. Upload & Scaffold with Gemini 2.5 Pro

1. Upload fastmcp_source.txt in Google AI Studio’s Files panel.
2. Copy the returned file ID (e.g., files/abcd1234).
3. Prompt Gemini in a “Code Generation” notebook:

<<file:files/abcd1234>>
Generate a Python MCP server using FastMCP v2.x that:
1. Exposes a github_directory_structure tool.
2. Includes proper @mcp.tool() decorators.
3. Provides requirements.txt and Dockerfile.

Gemini 2.5 Pro will emit a multi‑file scaffold you can download, review, and immediately run.

6. Deploying in Production

• Containerize with the generated Dockerfile.
• Switch transports (e.g., HTTP) by wrapping mcp.app in FastAPI.
• Secure endpoints via API keys or OAuth middleware.

Final Checklist

• gitingest v0.1.4+ via pipx
• server.py uses FastMCP and @mcp.tool()
• Dependencies installed & uv run server.py succeeds
• AI Studio prompt references the correct file ID
• Local test returns a valid directory tree

Now you have it: a robust, custom MCP server—ready to power any LLM‑based application.

Until the next one,

‍
Cohorte Team

April 17, 2025