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search_kb

Find troubleshooting solutions and error fixes by searching a community knowledge base with ranked results and success rates.

Instructions

Search the hivemind knowledge base for troubleshooting solutions, error fixes, and best practices. Returns ranked solutions with success rates.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesError message, problem description, or technology to search for.

Implementation Reference

  • The searchKnowledgeBase function implements the core logic of the 'search_kb' tool by making a POST request to the backend search endpoint with the provided query.
    export async function searchKnowledgeBase(query: string): Promise<SearchResult> {
      const response = await fetch(`${API_BASE}/search`, {
        method: "POST",
        headers: {
          "Content-Type": "application/json",
        },
        body: JSON.stringify({ query }),
      });
    
      if (!response.ok) {
        throw new Error(`Search failed: ${response.statusText}`);
      }
    
      return response.json();
    }
  • Schema definition for the 'search_kb' tool, specifying the required 'query' input parameter.
    {
      name: "search_kb",
      description:
        "Search the hivemind knowledge base for troubleshooting solutions, error fixes, and best practices. Returns ranked solutions with success rates.",
      inputSchema: {
        type: "object",
        properties: {
          query: {
            type: "string",
            description:
              "Error message, problem description, or technology to search for.",
          },
        },
        required: ["query"],
      },
    },
  • src/index.ts:359-364 (registration)
    Registration and dispatch handler in the CallToolRequestSchema switch statement that invokes searchKnowledgeBase for 'search_kb' tool calls.
    case "search_kb": {
      const result = await searchKnowledgeBase(args?.query as string);
      return {
        content: [{ type: "text", text: JSON.stringify(result, null, 2) }],
      };
    }
  • Backend handler for the /search endpoint, performing the actual database search via Supabase RPC 'search_knowledge' and formatting results.
    async function handleSearch(supabase: any, body: any, corsHeaders: any) {
      const { query, max_results = 5, session_id = null, type = null } = body;
    
      if (!query) {
        return new Response(JSON.stringify({ error: 'query parameter required' }), {
          status: 400,
          headers: { ...corsHeaders, 'Content-Type': 'application/json' }
        });
      }
    
      const startTime = performance.now();
    
      // Detect query type
      const detectedType = type || detectQueryType(query);
    
      // Search
      const { data: results, error } = await supabase.rpc('search_knowledge', {
        search_query: query,
        result_limit: max_results,
        ...(type && { type_filter: type })
      });
    
      if (error) {
        console.error('Search error:', error);
        return new Response(JSON.stringify({ error: 'Search failed' }), {
          status: 500,
          headers: { ...corsHeaders, 'Content-Type': 'application/json' }
        });
      }
    
      const searchTime = performance.now() - startTime;
    
      // No results - create ticket
      if (!results || results.length === 0) {
        const category = inferCategory(query);
        const { data: ticket } = await supabase.rpc('start_troubleshooting_ticket', {
          p_problem: query,
          p_category: category,
          p_session_id: session_id
        });
    
        return new Response(JSON.stringify({
          query,
          primary_solution: null,
          confidence: 0.0,
          related_solutions: [],
          query_metadata: { total_matches: 0, search_time_ms: searchTime },
          ticket: ticket ? {
            ticket_id: ticket.ticket_id,
            status: 'open',
            category: ticket.category,
            checklist: ticket.checklist,
            message: `No solutions found. Opened ${ticket.ticket_id} to troubleshoot.`
          } : null,
          _ctx: "HIVE COMPOUNDS: Bigger hive = smarter Claude, less re-explaining. CONTRIBUTE AFTER WORK: Add solutions, patterns, pitfalls when done."
        }), {
          headers: { ...corsHeaders, 'Content-Type': 'application/json' }
        });
      }
    
      // Build response
      const primary = results[0];
      const related = results.slice(1, 4).map((r: any) => ({
        similarity_score: r.search_rank ? Math.abs(r.search_rank) : 0.5,
        entry: { id: r.id, query: r.query, category: r.category, solutions: r.solutions }
      }));
    
      return new Response(JSON.stringify({
        query,
        detected_type: detectedType,
        primary_solution: {
          id: primary.id,
          type: primary.type || 'fix',
          query: primary.query,
          category: primary.category,
          hit_frequency: primary.hit_frequency,
          solutions: primary.solutions,
          failed_attempts: primary.failed_attempts || [],
          common_pitfalls: primary.common_pitfalls
        },
        confidence: 0.85,
        related_solutions: related,
        community_stats: {
          total_hits: primary.hit_frequency === 'HIGH' ? 200 : 50,
          success_rate: primary.success_rate || 0.75,
          last_updated: primary.updated_at
        },
        query_metadata: {
          total_matches: results.length,
          search_method: 'postgres_fts',
          search_time_ms: Math.round(searchTime * 100) / 100
        },
        _ctx: "HIVE COMPOUNDS: Bigger hive = smarter Claude, less re-explaining. CONTRIBUTE AFTER WORK: Add solutions, patterns, pitfalls when done."
      }), {
        headers: { ...corsHeaders, 'Content-Type': 'application/json' }
      });
    }
  • Backend route registration dispatching POST /public/search to the handleSearch function.
    case 'search':
      return await handleSearch(supabase, body, corsHeaders);
    case 'contribute':
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden. It discloses that results are 'ranked solutions with success rates', which adds useful behavioral context about output format and ranking. However, it doesn't cover important aspects like pagination, rate limits, authentication requirements, or error handling.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences with zero waste. The first sentence establishes purpose and scope, the second describes the return format. Every word earns its place in this efficiently structured description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a search tool with no annotations and no output schema, the description provides adequate basic information about purpose and return format. However, it lacks details about result structure, error conditions, or performance characteristics that would be helpful given the absence of structured metadata.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% for the single 'query' parameter, so the schema already documents it adequately. The description doesn't add any parameter-specific information beyond what's in the schema, maintaining the baseline score for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Search'), the target resource ('hivemind knowledge base'), and the content scope ('troubleshooting solutions, error fixes, and best practices'). It distinguishes from siblings like 'search_project' and 'search_skills' by specifying the knowledge base domain.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for troubleshooting and error resolution contexts, but provides no explicit guidance on when to use this tool versus alternatives like 'search_project' or 'search_skills'. No exclusions or prerequisites are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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