Un servidor MCP que ayuda a los ingenieros de soporte a encontrar problemas similares en GitHub para acelerar la resolución de problemas.
Instalar dependencias:
npm installEstablezca su token de GitHub como una variable de entorno:
export GITHUB_TOKEN=your_github_personal_access_tokenConstruir el servidor:
npm run buildActualice la configuración del escritorio de Claude, por ejemplo, code ~/Library/Application\ Support/Claude/claude_desktop_config.json
Actualícelo para incluir la ruta completa en la que se clonó este repositorio:
{
"mcpServers": {
"find-similar-github-issues": {
"command": "node",
"args": [
"/Users/<repo_path>/build/index.js"
]
}
}
}Related MCP server: OSSInsight MCP Server
Busca problemas similares en un repositorio de GitHub según la descripción del problema.
Calcula puntuaciones de similitud para clasificar los resultados
Devuelve detalles del problema formateados con enlaces
El servidor proporciona una herramienta:
Encuentra problemas de GitHub similares a una descripción dada.
Parámetros:
owner : propietario/organización del repositorio de GitHub
repo : nombre del repositorio de GitHub
issueDescription : Descripción del problema para encontrar problemas similares.
maxResults : Número máximo de problemas similares a devolver (predeterminado: 5)
Esta implementación utiliza un coeficiente de similitud de Jaccard simple para comparar texto. Para uso en producción, considere implementar técnicas de PLN más sofisticadas para una mejor coincidencia de similitudes.
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.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0find-similar-issuesScored across 1 tool
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.
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.
