GitHub MCP Server

Overview

Spring AI MCP PoC is a lightweight proof‑of‑concept server that demonstrates how an application built on the Spring AI framework can seamlessly talk to a Model Context Protocol (MCP) server and, in turn, interact with GitHub via the MCP’s toolset. The core problem it solves is the friction that developers face when trying to embed GitHub‑specific intelligence into an LLM workflow: normally a developer would need to write custom REST clients, handle authentication, and translate user prompts into API calls. This PoC abstracts all that complexity by exposing a single REST endpoint that accepts natural‑language prompts and optionally enables MCP tools. Behind the scenes, Spring AI forwards the prompt to an OpenAI model, and if requested, the MCP server injects GitHub‑related tools into the LLM’s context so that function calling can be used to fetch repository data, list issues, or create pull requests—all without the LLM developer writing any GitHub API code.

The server’s value lies in its minimal configuration and tight integration with two powerful ecosystems. By leveraging Spring Boot’s dependency injection, the PoC can be built and run with a single command while still allowing developers to override credentials or switch LLM providers via environment variables. The MCP integration means that any tool defined by the GitHub MCP server becomes instantly available to the LLM, and new tools can be added without redeploying the application. This makes it an ideal platform for experimenting with “GitHub‑first” AI assistants that can read code, analyze pull requests, or automate repository maintenance directly from a chat interface.

Key capabilities of the PoC include:

• Spring AI orchestration: Simplifies interaction with LLMs and manages prompt formatting.
• MCP tool discovery: The endpoint lists all available GitHub tools, allowing developers to introspect what the LLM can do.
• Function calling support: When is true, the LLM can invoke GitHub operations as if they were native functions, returning structured JSON back to the client.
• RESTful API surface: Exposes health checks, prompt processing, and tool enumeration through clean HTTP endpoints.
• Configurable credentials: Supports optional GitHub tokens for authenticated operations and a configurable MCP server URL, making it flexible across staging, development, or production environments.

Typical use cases span from building a conversational chatbot that can pull the latest status of a repository, to automating code review workflows where the LLM recommends changes and the MCP server applies them. In continuous‑integration pipelines, developers could query the LLM for insights on test coverage or dependency updates, while the MCP layer handles the GitHub API calls. The PoC therefore serves as both a learning tool for developers new to MCP and a functional foundation that can be extended into production‑grade services.

By unifying Spring AI, OpenAI’s language models, and GitHub MCP tools into a single, easy‑to‑deploy server, Spring AI MCP PoC lowers the barrier to creating intelligent, GitHub‑aware assistants. It showcases how modern LLMs can be coupled with external data sources through a standardized protocol, enabling richer interactions without sacrificing developer productivity.