Searches GitHub repositories for similar issues based on issue descriptions, calculates similarity scores, and returns formatted issue details with links to help support engineers speed up troubleshooting.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@GitHub Support Assistant find similar issues to 'login fails after password reset' in facebook/react"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
An MCP server that helps support engineers find similar GitHub issues to speed up troubleshooting.
Install dependencies:
npm installSet your GitHub token as an environment variable:
export GITHUB_TOKEN=your_github_personal_access_tokenBuild the server:
npm run buildUpdate the claude desktop configuration, e.g.
code ~/Library/Application\ Support/Claude/claude_desktop_config.json
Update it to include the full path that this repository was cloned to:
{
"mcpServers": {
"find-similar-github-issues": {
"command": "node",
"args": [
"/Users/<repo_path>/build/index.js"
]
}
}
}Related MCP server: OSSInsight MCP Server
Searches for similar issues in a GitHub repository based on issue description
Calculates similarity scores to rank results
Returns formatted issue details with links
The server provides one tool:
Finds GitHub issues similar to a given description.
Parameters:
owner: GitHub repository owner/organization
repo: GitHub repository name
issueDescription: Description of the issue to find similar ones for
maxResults: Maximum number of similar issues to return (default: 5)
This implementation uses a simple Jaccard similarity coefficient to compare text. For production use, consider implementing more sophisticated NLP techniques for better similarity matching.
Find GitHub issues similar to a new issue description
| Name | Required | Description | Default |
|---|---|---|---|
| issueDescription | Yes | Description of the issue to find similar ones for | |
| maxResults | No | Maximum number of similar issues to return | |
| owner | Yes | GitHub repository owner/organization | |
| repo | Yes | GitHub repository name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure but only states the basic function. It lacks details on how similarity is determined (e.g., semantic matching, keywords), performance characteristics (e.g., response time, rate limits), or error handling, leaving significant gaps for a tool with potential complexity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no wasted words, efficiently conveying the core purpose. It is appropriately sized and front-loaded, making it easy to understand at a glance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is insufficient for a tool that performs similarity matching—a non-trivial operation. It omits critical details like return format, matching algorithm, or error cases, leaving the agent with incomplete context for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the input schema fully documents all parameters. The description adds no additional meaning beyond what the schema provides, such as explaining the relationship between parameters or usage nuances, meeting the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Find') and resource ('GitHub issues similar to a new issue description'), making the purpose immediately understandable. However, without sibling tools for comparison, it cannot differentiate from alternatives, preventing a score of 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, prerequisites, or contextual constraints. It simply restates the tool's function without offering usage instructions or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
With only one tool, there is no possibility of confusion or overlap between tools, making disambiguation perfect. The tool's purpose is clearly defined and singular.
The single tool name follows a consistent verb-noun pattern (find-similar-issues), and with no other tools to compare, there is no inconsistency. The naming is clear and predictable.
A single tool is too few for a server named 'GitHub Support Assistant', which implies broader functionality beyond just finding similar issues. This minimal set feels thin and incomplete for the apparent scope.
The tool set is severely incomplete for the domain of GitHub support, lacking essential operations like creating issues, commenting, searching repositories, or managing pull requests. It covers only a narrow aspect, leading to dead ends for agents.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Code intelligence for LLMs. Analyze, search, and retrieve code from any public git repository.
Finds real, maintained open-source repos that fit your project. MCP grounding for coding agents.
Screens public GitHub repos and PRs to generate risk maps, findings, and merge-readiness signals.
Ask any GitHub repository a question. Get source-backed answers.
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