MCP Test – GitHub API Integration Demo

MCP Test is a lightweight Node.js server designed to illustrate how an MCP (Model Context Protocol) service can seamlessly connect an AI assistant to external APIs—in this case, GitHub. By exposing a minimal set of endpoints that authenticate with GitHub and validate access tokens, the server demonstrates how developers can embed real‑world data sources into conversational AI workflows without exposing credentials or handling OAuth flows directly within the assistant.

The core problem this MCP solves is secure, authenticated access to third‑party APIs. AI assistants often need up-to-date information from services like GitHub, but directly embedding API keys in prompts or client code poses security risks. MCP Test centralizes token management: a configuration file holds the GitHub personal access token, and the server authenticates once, returning a verified user identity. This pattern lets an assistant query the MCP to confirm token validity before proceeding with any GitHub‑specific logic, ensuring that only authorized requests reach the external service.

Key features of MCP Test include:

• Token loading from a standardized configuration path (), keeping secrets out of source control.
• GitHub authentication via Octokit, the official GitHub SDK for Node.js, which handles HTTP communication and error handling internally.
• Token validation by retrieving the authenticated user’s profile, providing a quick health check that the token is active and has sufficient scopes.
• Simplicity: the server exposes a single, well‑defined endpoint that can be invoked by an AI client to confirm authentication status.

Real‑world use cases are plentiful. A developer might build a “GitHub Assistant” that can create issues, pull requests, or fetch repository statistics on demand. By routing all GitHub interactions through MCP Test, the assistant remains stateless and secure: it never stores or transmits raw tokens. Instead, it asks the MCP for a short‑lived session token or confirmation that the current credentials are valid, then performs the desired operation. This pattern scales to other services—any API that requires authentication can be wrapped in a similar MCP server, allowing AI assistants to act as orchestrators rather than direct API consumers.

Integration into existing AI workflows is straightforward. The assistant sends a request to the MCP endpoint, receives a JSON payload indicating success or failure, and then proceeds with higher‑level logic. Because MCP Test follows the standard Model Context Protocol conventions—exposing resources, tools, and prompts—it can be discovered automatically by MCP‑enabled clients. Developers benefit from a clean separation of concerns: the assistant focuses on natural language understanding, while the MCP server handles authentication and API communication. This modularity enhances maintainability, security, and compliance with best practices for handling sensitive credentials.