AI Research MCP Server

Instructions

Implementation Reference

• src/ai_research_mcp/server.py:395-419 (handler)

  Core handler function that implements the search_github_repos tool logic: caches results from GithubClient.search_repositories and formats them.

  async def _search_github_repos(
      self,
      keywords: Optional[List[str]] = None,
      topics: Optional[List[str]] = None,
      min_stars: int = 50,
      days: int = 30,
      max_results: int = 25,
  ) -> str:
      """Search GitHub repositories."""
      cache_key = f"github_{keywords}_{topics}_{min_stars}_{days}"
      cached = self.cache.get(cache_key, self.cache_expiry["github"])
      if cached:
          repos = cached
      else:
          repos = await asyncio.to_thread(
              self.github.search_repositories,
              keywords=keywords,
              topics=topics,
              min_stars=min_stars,
              pushed_since=f"{days}d",
              max_results=max_results,
          )
          self.cache.set(cache_key, repos)
      
      return self._format_repos(repos)

• src/ai_research_mcp/server.py:87-120 (registration)

  Tool registration in list_tools(), including name, description, and input schema definition.

  Tool(
      name="search_github_repos",
      description="Search for trending AI/ML GitHub repositories",
      inputSchema={
          "type": "object",
          "properties": {
              "keywords": {
                  "type": "array",
                  "items": {"type": "string"},
                  "description": "Keywords to search for",
              },
              "topics": {
                  "type": "array",
                  "items": {"type": "string"},
                  "description": "GitHub topics to filter by (e.g., ['llm', 'transformer'])",
              },
              "min_stars": {
                  "type": "integer",
                  "description": "Minimum number of stars",
                  "default": 50,
              },
              "days": {
                  "type": "integer",
                  "description": "Look for repos updated in last N days",
                  "default": 30,
              },
              "max_results": {
                  "type": "integer",
                  "description": "Maximum number of results",
                  "default": 25,
              },
          },
      },
  ),

• src/ai_research_mcp/server.py:623-647 (helper)

  Helper function used by the handler to format the list of repositories as markdown.

  def _format_repos(self, repos: List[Dict]) -> str:
      """Format repositories as markdown."""
      if not repos:
          return "*No repositories found.*"
      
      lines = []
      for i, repo in enumerate(repos, 1):
          name = repo.get("full_name", "Unknown")
          description = repo.get("description", "No description")
          url = repo.get("url", "")
          stars = repo.get("stars", 0)
          language = repo.get("language", "")
          topics = repo.get("topics", [])
          
          lines.append(f"### {i}. [{name}]({url})")
          lines.append(f"⭐ {stars:,} • {language}")
          lines.append(f"\n{description}")
          
          if topics:
              topic_tags = " ".join(f"`{t}`" for t in topics[:5])
              lines.append(f"\n{topic_tags}")
          
          lines.append("")
      
      return "\n".join(lines)