AI Research MCP Server

Instructions

Implementation Reference

• src/ai_research_mcp/server.py:458-478 (handler)

  The primary handler for the 'get_trending_models' tool. It manages caching, calls the HuggingFace client method, and formats the output using _format_models.

  async def _get_trending_models(
      self,
      task: Optional[str] = None,
      sort: str = "downloads",
      limit: int = 30,
  ) -> str:
      """Get trending models from Hugging Face."""
      cache_key = f"hf_models_{task}_{sort}"
      cached = self.cache.get(cache_key, 3600)
      if cached:
          models = cached
      else:
          models = await asyncio.to_thread(
              self.huggingface.get_trending_models,
              task=task,
              sort=sort,
              limit=limit,
          )
          self.cache.set(cache_key, models)
      
      return self._format_models(models)

• src/ai_research_mcp/server.py:159-182 (registration)

  Registration of the 'get_trending_models' tool, including name, description, and input schema definition.

  Tool(
      name="get_trending_models",
      description="Get trending AI models from Hugging Face",
      inputSchema={
          "type": "object",
          "properties": {
              "task": {
                  "type": "string",
                  "description": "Filter by task (e.g., 'text-generation', 'image-classification')",
              },
              "sort": {
                  "type": "string",
                  "enum": ["downloads", "likes", "trending", "created"],
                  "description": "Sort criterion",
                  "default": "downloads",
              },
              "limit": {
                  "type": "integer",
                  "description": "Maximum number of results",
                  "default": 30,
              },
          },
      },
  ),

• src/ai_research_mcp/data_sources/huggingface_client.py:97-147 (helper)

  Helper method in HuggingFaceClient that fetches and processes trending models using HfApi.list_models. Called by the server handler.

  def get_trending_models(
      self,
      task: Optional[str] = None,
      library: Optional[str] = None,
      sort: str = "downloads",
      limit: int = 50,
  ) -> List[Dict]:
      """Get trending models from Hugging Face.
      
      Args:
          task: Filter by task (e.g., 'text-generation', 'image-classification')
          library: Filter by library (e.g., 'pytorch', 'transformers')
          sort: Sort by 'downloads', 'likes', 'trending', or 'created'
          limit: Maximum number of results
          
      Returns:
          List of model dictionaries
      """
      try:
          models = self.api.list_models(
              filter=task,
              library=library,
              sort=sort,
              direction=-1,
              limit=limit,
          )
          
          results = []
          for model in models:
              # Get model info
              model_info = {
                  "id": model.id,
                  "author": model.author if hasattr(model, "author") else model.id.split("/")[0],
                  "model_name": model.modelId if hasattr(model, "modelId") else model.id.split("/")[-1],
                  "url": f"https://huggingface.co/{model.id}",
                  "downloads": model.downloads if hasattr(model, "downloads") else 0,
                  "likes": model.likes if hasattr(model, "likes") else 0,
                  "tags": model.tags if hasattr(model, "tags") else [],
                  "pipeline_tag": model.pipeline_tag if hasattr(model, "pipeline_tag") else None,
                  "library": model.library_name if hasattr(model, "library_name") else None,
                  "created_at": model.created_at.isoformat() if hasattr(model, "created_at") and model.created_at else None,
                  "last_modified": model.last_modified.isoformat() if hasattr(model, "last_modified") and model.last_modified else None,
                  "source": "huggingface",
              }
              results.append(model_info)
          
          return results
      except Exception as e:
          print(f"Error fetching models: {e}")
          return []

• src/ai_research_mcp/server.py:649-668 (helper)

  Helper function to format the list of models into a markdown string for the tool response.

  def _format_models(self, models: List[Dict]) -> str:
      """Format models as markdown."""
      if not models:
          return "*No models found.*"
      
      lines = []
      for i, model in enumerate(models, 1):
          model_id = model.get("id", "Unknown")
          url = model.get("url", "")
          downloads = model.get("downloads", 0)
          likes = model.get("likes", 0)
          task = model.get("pipeline_tag", "")
          
          lines.append(f"### {i}. [{model_id}]({url})")
          lines.append(f"📥 {downloads:,} downloads • ❤️ {likes} likes")
          if task:
              lines.append(f"Task: `{task}`")
          lines.append("")
      
      return "\n".join(lines)