Github Repo Summarizer

An MCP server that turns any public GitHub repository into a PDF report containing a codebase summary, the most recent commits, and suggested improvements — generated locally by an Ollama model.

What it does

The server exposes a single MCP tool:

summarize_github_repo(repo_url: str, output_dir: str = ".") -> str

Given a GitHub URL (e.g. https://github.com/owner/repo, owner/repo shorthand, or a [email protected]:owner/repo.git SSH URL), it:

1. Fetches repository metadata, the latest 25 commits, and the recursive file tree from the public GitHub API.
2. Picks a representative set of source files (up to 25 files, each ≤ 40 KB, total ≤ 400 KB), prioritising entry points and manifests (README, pyproject.toml, package.json, Dockerfile, etc.) and skipping noise (node_modules, dist, lockfiles, snapshot folders).
3. Sends that corpus to a local Ollama model in two passes — one for the codebase summary, one for improvement suggestions.
4. Renders a PDF (via ReportLab) and returns the absolute path.

The PDF contains

Section	What's in it
Title block	Repo full name, generation timestamp (UTC), link to the repo
Metadata table	Description, primary language, default branch, stars, forks, open issues, license, created/last-pushed dates
Codebase Summary	250–400 word LLM-written prose summary of architecture, modules, tech stack
Recent Commits	Table of the latest 25 commits (SHA, author, date, message)
Suggested Improvements	Numbered list of 5–10 actionable items grounded in the actual code

Requirements

• Python ≥ 3.11
• uv for dependency management
• Ollama running locally with at least one model pulled
• An MCP-aware client (e.g. Claude Desktop)

Installation

# 1. Clone or copy this folder
cd github-repo-summarizer

# 2. Install dependencies into a project-local .venv
uv sync

# 3. Configure environment
cp .env.example .env
# then edit .env to taste (see "Configuration" below)

# 4. Pull at least one Ollama model
ollama pull llama3.2          # small, fast
# or
ollama pull qwen2.5-coder:7b  # better for code analysis, ~4.7 GB

Make sure the Ollama server is running:

ollama serve          # in a terminal
# or just launch the Ollama.app — it starts the server automatically

Configuration

All settings come from environment variables, which can live in a .env file next to main.py (loaded automatically at startup).

Variable	Default	Purpose
OLLAMA_MODEL	llama3.2	Tag of the Ollama model to use. Must be pulled.
OLLAMA_HOST	http://localhost:11434	Ollama HTTP endpoint.
OLLAMA_NUM_CTX	16384	Context window in tokens. Bump for larger repos; lower if memory-constrained.

Example .env:

OLLAMA_MODEL=qwen2.5-coder:7b
OLLAMA_HOST=http://localhost:11434
OLLAMA_NUM_CTX=32768

Choosing a model

Local LLMs vary widely in code-comprehension quality. Suggestions:

• llama3.2 — 3 B parameters, fast, decent general summaries.
• llama3.1:8b — bigger, better narrative quality.
• qwen2.5-coder:7b — recommended for code-heavy repos; trained for code.
• codellama:13b — older but solid for code review-style output.

Make sure the model's native context window is ≥ OLLAMA_NUM_CTX; otherwise the corpus will be silently truncated.

Using it with Claude Desktop

Add the server to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) — adjust the absolute paths to wherever you cloned the project:

{
  "mcpServers": {
    "github-repo-summarizer": {
      "command": "/absolute/path/to/uv",
      "args": [
        "run",
        "--directory",
        "/absolute/path/to/github-repo-summarizer",
        "--frozen",
        "--with",
        "mcp[cli]",
        "mcp",
        "run",
        "/absolute/path/to/github-repo-summarizer/main.py"
      ]
    }
  }
}

Notes:

• Use the absolute path to uv (which uv). Claude Desktop is a GUI app and doesn't inherit your shell's PATH.
• --directory must point at the project directory, not the script itself. uv chdirs into it.
• Fully quit Claude Desktop (⌘Q on macOS) and reopen it after editing config. Just closing the window won't reload MCP servers.

Once connected, ask Claude to use it:

Use github-repo-summarizer to summarize https://github.com/anthropics/anthropic-sdk-python and save the PDF to my Desktop.

Claude will call summarize_github_repo(repo_url=..., output_dir=...) and return the path to the generated PDF.

Using it from any other MCP client

The server speaks MCP over stdio. Any client that can launch a subprocess and exchange JSON-RPC over stdin/stdout will work. The launch command is:

uv run --directory /path/to/github-repo-summarizer --frozen \
       --with mcp[cli] mcp run /path/to/github-repo-summarizer/main.py

For quick local testing:

uv run python main.py

…and pipe an MCP initialize request to stdin to confirm it responds.

Output

PDFs are written to <output_dir>/<owner>-<repo>-summary.pdf. output_dir defaults to the current working directory of the server process (which, when launched by Claude Desktop, may not be where you expect — pass an explicit path when invoking the tool).

Limitations

• Public repos only. Private repos would need authenticated GitHub access; this server doesn't ship that.
• File budget. The server reads up to 25 files (≤ 40 KB each, ≤ 400 KB total). Very large monorepos will be sampled, not exhaustively analysed.
• LLM quality is bounded by the model. A 3 B parameter model will produce shallower summaries than an 8 B+ model. Pick accordingly.
• No streaming. PDFs are produced after both LLM calls complete; expect tens of seconds to a couple of minutes per repo, dominated by the model's generation speed.
• PDF only. No HTML/Markdown output mode (yet).

Troubleshooting

Server shows "failed" in Claude Desktop developer settings. Check ~/Library/Logs/Claude/mcp-server-github-repo-summarizer.log for the real error. The two most common causes:

• --directory value missing or pointing at a file → uv exits with Not a directory. Fix the path in claude_desktop_config.json.
• uv not found → use the absolute path to uv in command.

Could not connect to Ollama at http://localhost:11434. Run ollama serve (or open Ollama.app).

Ollama model 'X' is not pulled. Run ollama pull X.

Truncated or empty summary. The corpus exceeded the model's context window. Lower OLLAMA_NUM_CTX only hurts; instead, switch to a model with a larger native context, or shrink MAX_TOTAL_BYTES in main.py.

Project layout

github-repo-summarizer/
├── main.py            # MCP server + GitHub/Ollama/PDF logic
├── pyproject.toml     # Project metadata and dependencies
├── .env               # Local config (gitignored)
├── .env.example       # Template
└── README.md

License

Not specified. Add one before redistributing.