research_topic

Instructions

Implementation Reference

• src/searxng_mcp/tools/search.py:174-285 (handler)

  Core implementation of the research_topic tool in the SearchTools class. Performs multiple searches based on specified depth, deduplicates results, formats raw source material, and provides instructions for further analysis.

  def research_topic(
      self,
      query: str,
      depth: Literal["quick", "standard", "deep"] = "standard"
  ) -> List[TextContent]:
      """Deep research with multiple searches and deduplication.
  
      Performs multiple searches with different strategies to gather
      comprehensive information from diverse sources. Automatically
      deduplicates results.
  
      Args:
          query: Research topic
          depth: Research depth
              - quick: 2 searches, ~15 unique results
              - standard: 4 searches, ~30 unique results
              - deep: 6 searches, ~50 unique results
  
      Returns:
          Deduplicated and aggregated research results
      """
      self.logger.info(f"Starting {depth} research on: {query}")
  
      all_results = []
      search_strategies = []
  
      # Define search strategies based on depth
      if depth == "quick":
          search_strategies = [
              {"category": "general", "engines": None},
              {"category": "news", "engines": None},
          ]
          max_per_search = 10
      elif depth == "standard":
          search_strategies = [
              {"category": "general", "engines": "google,bing"},
              {"category": "general", "engines": "duckduckgo,brave"},
              {"category": "news", "engines": None},
              {"category": "general", "engines": "wikipedia"},
          ]
          max_per_search = 10
      else:  # deep
          search_strategies = [
              {"category": "general", "engines": "google,bing"},
              {"category": "general", "engines": "duckduckgo,brave"},
              {"category": "news", "engines": "google,bing"},
              {"category": "news", "engines": "duckduckgo"},
              {"category": "general", "engines": "wikipedia"},
              {"category": "general", "engines": None},  # All engines
          ]
          max_per_search = 15
  
      # Execute all searches
      for strategy in search_strategies:
          try:
              results = self._search(
                  query,
                  category=strategy["category"],
                  engines=strategy["engines"]
              )
              all_results.extend(results.get("results", [])[:max_per_search])
          except Exception as e:
              self.logger.warning(f"Search strategy failed: {e}")
              continue
  
      # Deduplicate
      unique_results = self._deduplicate_results(all_results)
  
      # Format output - present as raw material to analyze, not numbered references
      output = f"🔬 RESEARCH DATA for analysis: {query}\n"
      output += f"📊 {len(unique_results)} unique sources gathered from {len(search_strategies)} search strategies\n\n"
      output += f"{'='*80}\n"
      output += f"RAW SOURCE MATERIAL (analyze and synthesize - do NOT list to user):\n"
      output += f"{'='*80}\n\n"
  
      for result in unique_results[:25]:
          output += f"• **{result.get('title', 'No title')}**\n"
          output += f"  URL: {result.get('url', '')}\n"
          if result.get('content'):
              content = result['content'][:100] + "..." if len(result['content']) > 100 else result['content']
              output += f"  Content: {content}\n"
  
          if result.get('publishedDate'):
              output += f"  Date: {result['publishedDate']}\n"
  
          output += "\n"
  
      if not unique_results:
          output += "No results found. Try a different query.\n"
  
      output += f"\n{'='*80}\n"
      output += f"⚠️  YOUR TASK: ANALYZE & SYNTHESIZE (NOT list sources!)\n"
      output += f"{'='*80}\n\n"
      output += f"You have {min(len(unique_results), 25)} sources above as RAW MATERIAL.\n\n"
      output += f"REQUIRED ANALYSIS PROCESS:\n"
      output += f"1. Read all source titles and content snippets above\n"
      output += f"2. Extract key claims and facts from the content\n"
      output += f"3. Cross-reference: What do MULTIPLE sources say? (HIGH confidence)\n"
      output += f"4. What's only in ONE source? (LOW confidence - note as unverified)\n"
      output += f"5. Any contradictions between sources? (flag for user)\n\n"
      output += f"REQUIRED OUTPUT FORMAT:\n"
      output += f"- Executive summary (2-3 sentences)\n"
      output += f"- Key findings with confidence indicators:\n"
      output += f"  ✓ HIGH (5+ sources agree)\n"
      output += f"  ~ MEDIUM (2-4 sources)\n"
      output += f"  ? LOW (single source only)\n"
      output += f"- Contradictions/uncertainties if any\n"
      output += f"- Brief conclusion\n\n"
      output += f"DO NOT output source URLs or numbered lists - synthesize into narrative!\n"
      output += f"{'='*80}\n"
  
      return [TextContent(type="text", text=output)]

• src/searxng_mcp/server.py:158-163 (registration)

  MCP tool registration for 'research_topic'. Uses @mcp.tool decorator with detailed description reference and defines input parameters with Pydantic validation via Annotated types, delegating execution to SearchTools instance.

  @self.mcp.tool(description=RESEARCH_TOPIC_DESC)
  def research_topic(
      query: Annotated[str, Field(description="Research topic or question")],
      depth: Annotated[Literal["quick", "standard", "deep"], Field(description="Research depth")] = "standard"
  ):
      return self.search_tools.research_topic(query, depth)

• src/searxng_mcp/server.py:47-82 (schema)

  Detailed description string for the research_topic tool, used in MCP registration. Includes comprehensive usage guidelines, parameter descriptions, depth options, and critical instructions for source analysis and synthesis.

  RESEARCH_TOPIC_DESC = """Deep research with multiple searches and source validation.
  
  Use this when:
  - User wants comprehensive research or briefing
  - Need to validate information across multiple sources
  - Looking for in-depth analysis
  - User asks to "research", "investigate", or "give me a briefing"
  
  This tool runs 2-6 searches automatically using different strategies:
  - Searches multiple engines (Google, Bing, DuckDuckGo, Brave, Wikipedia)
  - Searches both general web and news sources
  - Deduplicates results across all searches
  - Returns 15-50 UNIQUE sources depending on depth
  
  Perfect for creating comprehensive briefings with validated information.
  
  Parameters:
  query* - Research topic
  depth - Research thoroughness:
    • "quick" - 2 searches, ~15 unique sources
    • "standard" - 4 searches, ~30 unique sources (recommended)
    • "deep" - 6 searches, ~50 unique sources
  
  CRITICAL - After receiving sources, you MUST:
  1. Read and analyze ALL sources provided (titles, URLs, content snippets)
  2. Cross-reference claims across multiple sources
  3. Identify facts confirmed by many sources (high confidence)
  4. Note contradictions or single-source claims (lower confidence)
  5. Synthesize findings into a comprehensive briefing with:
     • Executive summary of key findings
     • Main facts/developments (note how many sources confirm each)
     • Contradictions or uncertainties
     • Source quality assessment (which engines found what)
  6. DO NOT just list the sources - you must analyze, validate, and synthesize them into actionable intelligence
  
  Returns: Research briefing with analyzed, validated, cross-referenced information"""