generate_weekly_summary

Instructions

Implementation Reference

• src/ai_research_mcp/server.py:536-562 (handler)

  The core handler function implementing the generate_weekly_summary tool logic: fetches weekly papers from multiple sources, trending repos, recent models, and formats into a markdown summary.

  async def _generate_weekly_summary(
      self,
      include_papers: bool = True,
      include_repos: bool = True,
      include_models: bool = True,
  ) -> str:
      """Generate weekly summary."""
      sections = []
      sections.append(f"# AI Research Weekly Summary\n*Generated: {datetime.now().strftime('%Y-%m-%d %H:%M')}*\n")
      
      if include_papers:
          hf_papers = await asyncio.to_thread(self.huggingface.get_daily_papers, days=7)
          arxiv_papers = await asyncio.to_thread(self.arxiv.get_latest_papers, days=7, max_results=50)
          pwc_papers = await asyncio.to_thread(self.papers_with_code.get_latest_papers, days=7)
          
          all_papers = hf_papers + arxiv_papers + pwc_papers
          sections.append(f"## 📄 This Week's Papers ({len(all_papers)})\n\n{self._format_papers(all_papers[:30])}")
      
      if include_repos:
          repos = await asyncio.to_thread(self.github.get_trending_repositories, period="weekly", max_results=30)
          sections.append(f"## 🔥 Trending Repositories ({len(repos)})\n\n{self._format_repos(repos[:20])}")
      
      if include_models:
          models = await asyncio.to_thread(self.huggingface.get_recent_models, days=7, limit=25)
          sections.append(f"## 🤖 New & Updated Models ({len(models)})\n\n{self._format_models(models[:15])}")
      
      return "\n\n".join(sections)

• src/ai_research_mcp/server.py:236-259 (registration)

  Tool registration in list_tools() handler, defining the tool name, description, and input schema.

  Tool(
      name="generate_weekly_summary",
      description="Generate a comprehensive weekly summary of AI research activity",
      inputSchema={
          "type": "object",
          "properties": {
              "include_papers": {
                  "type": "boolean",
                  "description": "Include papers section",
                  "default": True,
              },
              "include_repos": {
                  "type": "boolean",
                  "description": "Include GitHub repos section",
                  "default": True,
              },
              "include_models": {
                  "type": "boolean",
                  "description": "Include Hugging Face models section",
                  "default": True,
              },
          },
      },
  ),

• src/ai_research_mcp/server.py:239-259 (schema)

  Input schema defining optional boolean parameters for including papers, repos, and models in the summary.

      inputSchema={
          "type": "object",
          "properties": {
              "include_papers": {
                  "type": "boolean",
                  "description": "Include papers section",
                  "default": True,
              },
              "include_repos": {
                  "type": "boolean",
                  "description": "Include GitHub repos section",
                  "default": True,
              },
              "include_models": {
                  "type": "boolean",
                  "description": "Include Hugging Face models section",
                  "default": True,
              },
          },
      },
  ),

• src/ai_research_mcp/server.py:280-281 (handler)

  Dispatch logic in the main call_tool handler that invokes the weekly summary generator.

  elif name == "generate_weekly_summary":
      result = await self._generate_weekly_summary(**arguments)