GitHub Support Assistant

Overview

The Mcp Find Similar Github Issues server fills a critical gap for support engineers and developers who need to resolve GitHub issues quickly. In many projects, troubleshooting begins by searching the repository’s issue tracker for similar problems that have already been reported. Manually combing through thousands of issues is time‑consuming and error‑prone, especially when the relevant issue descriptions are not obvious or are phrased differently. This MCP server automates that search by accepting a natural‑language description of an issue and returning the most closely matching open issues from a specified repository. By presenting the results ranked by similarity score, it enables engineers to jump straight to relevant discussions, view existing solutions or workarounds, and avoid reinventing the wheel.

At its core, the server exposes a single tool—. The tool takes four parameters: the repository owner, the repository name, a textual description of the issue in question, and an optional maximum number of results to return. Internally it tokenizes the input description and compares it against all open issues in the target repository using a Jaccard similarity coefficient. The output is a neatly formatted list of issue titles, URLs, and a numerical score indicating how closely each issue matches the query. Because the server runs locally and requires only a GitHub personal access token, it respects privacy and security constraints while still accessing the full repository history.

For developers working with AI assistants like Claude, integrating this MCP server is straightforward. Once the server is running, it can be invoked directly from the assistant’s prompt or through a custom tool call. This means that an engineer can ask the AI, “Find me issues similar to this error message,” and receive a curated list of GitHub issues without leaving the chat interface. The AI can then summarize the top hits, suggest next steps, or even draft a new issue that incorporates insights from the similar ones. This tight coupling between AI reasoning and real‑world data accelerates debugging cycles, reduces duplicated work, and improves knowledge sharing within teams.

Real‑world scenarios where this server shines include:

• Rapid onboarding – New contributors can quickly discover existing discussions that mirror the problems they encounter.
• Incident response – During a production outage, support engineers can locate past incidents that match the symptoms and apply proven fixes.
• Feature requests – Product managers can see how often similar feature ideas have been raised, aiding prioritization decisions.
• Code reviews – Reviewers can surface related issues that may impact the change under review.

Unique advantages of this MCP server are its lightweight design, reliance on standard GitHub APIs, and the ability to run locally with minimal configuration. By leveraging a simple yet effective similarity metric, it delivers actionable insights in seconds, making it an indispensable tool for any team that relies on GitHub issue tracking as a knowledge base.