relix23/Github-MCP-Server

Github-MCP-Server: Lightweight MCP for GitHub APIs in Python

🚀 A light weight Model Context Protocol (MCP) server built with FastMCP to enable GitHub related operations.

Table of contents

• Overview
• What is MCP and why it helps
• Key features
• Architecture and design principles
• Getting started
• Quick start guide
• Working with the API
• Endpoints and request examples
• Configuration and deployment
• Performance and reliability
• Security considerations
• Testing and quality assurance
• Observability and logging
• Extending and contributing
• Roadmap
• Ecosystem and integrations
• Licensing and attribution

Overview The Github-MCP-Server project provides a compact, fast, and reliable MCP server crafted to enable smooth integration with GitHub data and workflows. Built on FastMCP, the server focuses on low latency, predictable resource use, and clear, maintainable code. It serves as a bridge between GitHub's ecosystem and client applications that rely on the MCP pattern to fetch, transform, cache, and route data.

What is MCP and why it helps Model Context Protocol (MCP) is a protocol that lets clients request model-contextual data from a server. It’s designed for simplicity, flexibility, and performance. In practice, MCP helps teams:

• Access GitHub data with predictable latency
• Compose data from multiple sources into a single context
• Cache results to reduce API calls and rate-limit pressure
• Create testable, modular data pipelines
• Separate concerns between data retrieval, transformation, and presentation

Key concepts in Github-MCP-Server

• Context: A data object that represents a GitHub resource or a composed view (repos, users, issues, pull requests, commits, and more).
• Model: A typed representation of a resource or a derived view used by clients.
• Protocol: The rules that govern how clients request context, and how the server resolves, caches, and returns results.
• FastMCP: The foundation that powers asynchronous, high-performance message passing and request handling in this server.

Why this project matters GitHub hosts a vast amount of data. Clients often need a stable, well-defined layer to access and combine data. This project provides that layer with a light footprint, clear structure, and an approachable API. It is ideal for tooling, automation, dashboards, and internal integrations that want to decouple GitHub API calls from application logic.

Key features

• Lightweight MCP server with a clean Python implementation
• FastMCP-based core for fast, scalable request handling
• GitHub-centric context models (users, repos, issues, pull requests, comments, workflows)
• Context composition and transformation helpers
• Local caching strategy with TTL and cache invalidation hooks
• Configurable endpoints and data providers
• Simple, readable API that maps well to GitHub data structures
• Extensible plugin system for additional providers and data sources
• Basic observability: structured logs, metrics hooks, and health checks
• Easy deployment in containers and on bare metal

Architecture and design principles

• Clarity first: The code base is organized to be easy to read and extend.
• Separation of concerns: Data retrieval, transformation, caching, and protocol handling live in their own modules.
• Asynchronous by default: Non-blocking I/O to maximize throughput on modern hardware.
• Determinism: Deterministic behavior for the same request yields the same result unless TTL or policy changes.
• Safe defaults: Sensible defaults reduce the risk of misconfiguration.
• Easy to test: Components are designed to be independently testable.
• Observability by default: Logging and metrics are integrated from the start.

Getting started This project targets developers who want a lightweight MCP server to expose GitHub-related data in a stable MCP API. It is written in Python and uses FastMCP for its core networking primitives. The server is designed to be simple to set up for experimentation and to scale through configuration and plugin support.

Prerequisites

• Python 3.10 or newer
• Git for cloning the repository
• Access to a running GitHub API token, if connecting to private resources
• A supported runtime environment (local machine, server, or container)

Installation

• Clone the repository
  • git clone https://github.com/relix23/Github-MCP-Server.git
• Create a virtual environment
  • python -m venv venv
  • source venv/bin/activate (Linux/macOS) or .\venv\Scripts\activate (Windows)
• Install dependencies
  • pip install -r requirements.txt
• Optional: configure a local development environment with environment variables (see Configuration)

Quick start guide

• Start the server in development mode
  • python -m github_mcp_server --config config/development.yaml
• Verify the server is running
  • curl -s http://localhost:8080/health
  • The response should indicate a healthy status
• Explore a sample context
  • Use the MCP client (or a curl-based test) to fetch a GitHub user context or a repository context
  • Example (conceptual): GET /context/github/user/{username}
• Observe logs and behavior
  • The server prints structured logs to stdout by default
  • Redirect logs to a file in production

Downloading the release assets The project provides prebuilt releases that simplify setup. From the latest releases, you can download a packaged binary or archive that contains a ready-to-run server. The releases page is the single source of truth for tested builds and packaging formats. For the latest builds, visit the Releases page: https://github.com/relix23/Github-MCP-Server/releases

From the Releases page, download and execute the release artifact

• Example file (fictional for illustration): Github-MCP-Server-0.2.0-linux-x86_64.tar.gz
• Steps:
  1. Download the file from the Releases page
  2. Extract: tar -xzf Github-MCP-Server-0.2.0-linux-x86_64.tar.gz
  3. Run: ./github-mcp-server
• If you prefer containerized runs, there may be a Docker image or similar artifact in the releases; follow the container-specific instructions provided there
• After download, ensure the binary has execute permissions:
  • chmod +x github-mcp-server

Note: The link above contains a path. If you follow that link, you will land on a page that lists release assets. The file you download is a prebuilt package intended to run as a standalone server or as part of a containerized workflow.

Working with the API The Github-MCP-Server exposes an MCP-style interface to fetch, compose, and serve GitHub-related data. The API is designed to be approachable for developers who know GitHub’s data model and REST conventions. Below is a high-level outline of how to use and extend the API.

Context models

• UserContext: Represents a GitHub user, including profile info, team membership, and related organizations.
• RepoContext: Represents a GitHub repository, including metadata, owners, topics, and collaborators.
• IssueContext: Represents a GitHub issue, including title, state, comments, labels, and assignees.
• PRContext: Represents a pull request with its status, review progress, and linked issues.
• CommentContext: Represents comments on issues, pull requests, and commits.
• WorkflowContext: Represents GitHub Actions workflows and their runs.
• CommitContext: Represents a commit object with metadata, author, and changes.

Context composition

• The server can compose multiple contexts into a single, higher-level context.
• Example: A context that includes a repo, its owners, and recent issues, useful for dashboards.

Caching strategy

• Time-to-live (TTL) controls how long a context remains cached.
• Cache invalidation hooks respond to GitHub webhook events or policy changes.
• Cache keys are stable and deterministic to ensure repeatability.

Endpoints and request patterns

• The MCP endpoints follow a consistent naming pattern that maps to GitHub resources.
• Common patterns:
  • GET /context/github/{resource_type}/{identifier}
  • POST /context/github/{resource_type}/{identifier}/transform
  • GET /cache/{key}
  • POST /admin/reload-contexts
• Endpoints are designed to be stitched into larger data flows or used standalone.
• Each response includes metadata about the source, freshness, and any applied transformations.

Usage patterns

• Read-through caching: The server first checks the cache, then fetches from GitHub if needed.
• On-demand composition: Clients request a composite context built from multiple sources.
• Lazy evaluation: Contexts are computed when requested, ensuring up-to-date results when possible.
• Observability hooks: The server emits events and metrics when contexts are created, updated, or invalidated.

Example requests

• Fetch a user context
  • GET /context/github/user/octocat
  • Response includes user profile details, organizations, and associated repos
• Fetch a repository context with issues
  • GET /context/github/repo/relix23/Github-MCP-Server?include=issues,prs,contributors
  • Response includes repo metadata, issues, pull requests, and contributor stats
• Transform a context
  • POST /context/github/repo/relix23/Github-MCP-Server/transform
  • Body: { "selector": ["issues_opened_last_30_days"] }

Sample code for a client (conceptual)

• Python: Using an MCP client to request a context

  • from mcp_client import McpClient
  • client = McpClient("http://localhost:8080")
  • user_ctx = client.get_context("github/user", "octocat")
  • print(user_ctx.data)

• Node.js: Fetching a repo context

  • const fetch = require('node-fetch')
  • const res = await fetch('http://localhost:8080/context/github/repo/relix23/Github-MCP-Server')
  • const data = await res.json()
  • console.log(data)

Configuration and deployment Configuring the server requires some careful decisions about how you want the server to operate in your environment. The configuration model emphasizes clarity and simplicity.

Configuration files

• development.yaml: Built for local testing.
• production.yaml: Safe defaults suitable for production deployments.
• Each configuration file contains sections for:
  • server: host, port, TLS, and protocol settings
  • github_provider: token, base url, rate limits
  • cache: TTL, eviction policy, size constraints
  • logging: level, format, sinks
  • metrics: enabled or disabled, intervals

Environment variables

• MCP_SERVER_HOST: The host the server binds to
• MCP_SERVER_PORT: The port
• GITHUB_TOKEN: Token used to access GitHub API for non-public data
• MCP_CACHE_TTL: Default TTL for cached contexts
• MCP_LOG_LEVEL: Logging level (debug, info, warning, error)

Docker and containerization

• A Dockerfile is provided to build a container image
• Build: docker build -t github-mcp-server:latest .
• Run: docker run -p 8080:8080 -e GITHUB_TOKEN=your_token github-mcp-server:latest
• Or use docker-compose with an accompanying docker-compose.yaml for local development

Security considerations

• Use TLS in production to protect traffic and credentials
• Keep GitHub access tokens in secure environments
• Limit exposed endpoints and enforce authentication on admin APIs
• Regularly rotate credentials and monitor for unusual activity
• Audit logs for access patterns and context usage

Observability and logging

• Structured logging in JSON for easy ingestion by log aggregators
• Health checks that reflect the status of the provider, cache, and protocol
• Basic metrics: request rate, cache hit/mail, context creation time
• Tracing support for distributed deployments (optional)

Testing and quality assurance

• Unit tests cover core MCP primitives, context composition, and caching
• Integration tests validate GitHub data fetch paths and error handling
• Property-based tests explore edge cases
• Continuous integration runs linting, static analysis, and test suites
• Local testing supports simulated GitHub responses to verify behavior

Extending and contributing

• The project expects contributors to follow the existing style guides
• Add new context models by extending the Context base class
• Write tests for new endpoints and transformations
• Update documentation as features evolve
• Submit pull requests with clear descriptions and linked issues

Development workflow highlights

• Feature branches model: feature/your-feature
• PRs reviewed by maintainers
• Clear versioning in releases with a changelog
• Emphasis on backward compatibility where possible
• Documentation is a first-class citizen; add docs for new features

Roadmap

• Add more GitHub data sources (events, gists, organizations)
• Improve the MCP client library with richer examples
• Introduce richer transform operations for context composition
• Expand observability with distributed tracing
• Provide official container images with security hardening

Ecosystem and integrations

• Integrates with common GitHub data workflows
• Works well with dashboards and monitoring stacks
• Plays nicely with other MCP-based services
• Supports plug-ins to fetch data from third-party sources

License

• This project is released under the MIT license.
• See LICENSE for details.
• Credits to the community that contributed ideas and code.

Community and support

• If you need help, open an issue on the repository
• For questions about design decisions or contribution guidelines, start a discussion
• Follow the repository for updates, improvements, and new releases

Appendix: Quick reference for developers

• Prerequisites: Python 3.10+, Git
• Commands you will use often:
  • git clone https://github.com/relix23/Github-MCP-Server.git
  • cd Github-MCP-Server
  • python -m venv venv
  • source venv/bin/activate
  • pip install -r requirements.txt
  • python -m github_mcp_server --config config/development.yaml
  • curl http://localhost:8080/health
• File structure highlights:
  • github_mcp_server/
    • init.py
    • api/
    • context/
    • providers/
    • transforms/
    • config/
    • tests/
    • docs/
  • scripts/
  • examples/

End-user guide: deployment patterns

• Local development: run on a laptop or workstation for testing and proof-of-concept work
• Staging environment: mirror production with a smaller data set to validate changes
• Production deployment: robust infrastructure with TLS, monitoring, and automated rollouts

Notes on reliability

• The server uses asynchronous patterns to maximize throughput and reduce latency
• Caching reduces load on GitHub and improves response times
• Clear error paths ensure clients can recover gracefully from transient failures

Operator checklist

• Verify TLS certificates and keys are valid
• Confirm GitHub token scope aligns with required access
• Ensure health checks are green and metrics are flowing to your monitoring system
• Confirm release artifacts are signed or hosted in a trusted environment
• Validate the behavior of critical data flows under load

Detailed API reference

• This project maintains a practical API layer that maps to GitHub concepts without leaking internal implementation details
• Endpoints are documented in the API module; see the inline API docs for specifics
• You will find examples in the docs directory to help developers experiment with contexts

System architecture diagram (textual)

• Client -> MCP Gateway -> Github-MCP-Server
• Github-MCP-Server -> GitHub API
• Github-MCP-Server -> Cache Layer
• Cache Layer -> Client
• Components communicate via asynchronous message passing
• The gateway handles routing, authentication, and basic transformations

Contributing guidelines (high level)

• Start with the issue tracker to discuss ideas
• Open a PR with a clear description and tests
• Keep changes small and well-scoped
• Include tests that demonstrate the new behavior
• Document any API changes in the docs

Acknowledgments

• The community around MCP, FastMCP, and GitHub API tooling
• Open-source projects that inspired the design of this server
• All maintainers and contributors who help keep the project healthy

Releases

• For the latest releases and prebuilt artifacts, visit the Releases page: https://github.com/relix23/Github-MCP-Server/releases
• The page lists assets, release notes, and upgrade guidance
• Use the assets to run the server directly or to integrate into your deployment workflow

End of README content.