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tadas-github

a2asearch-mcp

search_agents

Search the A2ASearch directory to find AI agents, MCP servers, CLI tools, and agent skills by query and type filtering.

Instructions

Search the A2ASearch directory for AI agents, MCP servers, CLI tools and agent skills. Returns name, description, type, stars, GitHub URL and capabilities for each result.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSearch query — e.g. 'database', 'browser automation', 'memory'
typeNoFilter by agent type (optional)
limitNoNumber of results to return (1-20, default 10)

Implementation Reference

  • Handler for the 'search_agents' tool, which fetches agent data from A2ASearch API and formats it into text content.
    if (name === "search_agents") {
      const { query, type, limit = 10 } = args;
      const params = new URLSearchParams({
        search: query,
        per_page: String(Math.min(20, Math.max(1, limit))),
      });
      if (type) params.set("type", type);
    
      const res = await fetch(`${API_BASE}/agents?${params}`);
      if (!res.ok) throw new Error(`API error: ${res.status}`);
      const data = await res.json();
    
      const results = data.data.map((a) => ({
        name: a.name,
        type: a.type,
        description: a.description,
        url: a.agentCardUrl,
        stars: a.stars,
        capabilities: a.capabilities?.slice(0, 5),
        languages: a.languages?.slice(0, 3),
        health_score: a.healthScore,
      }));
    
      return {
        content: [
          {
            type: "text",
            text:
              `Found ${data.pagination.total} results for "${query}"` +
              (type ? ` (type: ${type})` : "") +
              `\n\n` +
              results
                .map(
                  (r) =>
                    `**${r.name}** (${r.type})\n` +
                    `  ${r.description || "No description"}\n` +
                    `  ⭐ ${r.stars?.toLocaleString() || 0}  |  🔗 ${r.url}\n` +
                    (r.capabilities?.length
                      ? `  Capabilities: ${r.capabilities.join(", ")}\n`
                      : "")
                )
                .join("\n"),
          },
        ],
      };
    }
  • index.js:27-52 (registration)
    Tool registration for 'search_agents' in the ListToolsRequest handler, defining its name, description, and input schema.
    {
      name: "search_agents",
      description:
        "Search the A2ASearch directory for AI agents, MCP servers, CLI tools and agent skills. " +
        "Returns name, description, type, stars, GitHub URL and capabilities for each result.",
      inputSchema: {
        type: "object",
        properties: {
          query: {
            type: "string",
            description: "Search query — e.g. 'database', 'browser automation', 'memory'",
          },
          type: {
            type: "string",
            enum: ["MCP Server", "CLI Tool", "AI Coding Agent", "Agent Skill", "A2A Agent"],
            description: "Filter by agent type (optional)",
          },
          limit: {
            type: "number",
            description: "Number of results to return (1-20, default 10)",
            default: 10,
          },
        },
        required: ["query"],
      },
    },
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses the search behavior and return format, but lacks details on rate limits, authentication needs, pagination, or error handling. It adds basic context but does not fully compensate for the absence of annotations, leaving gaps in behavioral understanding.

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?

The description is front-loaded with the core purpose in the first sentence and adds return details in the second. Both sentences earn their place by providing essential information without redundancy or fluff, making it highly efficient and well-structured.

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

Completeness4/5

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

Given the tool's moderate complexity (search with filters), no annotations, and no output schema, the description is reasonably complete. It covers the purpose, scope, and return format, but could improve by addressing behavioral aspects like rate limits or error cases. It's adequate but has minor gaps in full contextual coverage.

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%, so the schema already documents all parameters thoroughly. The description does not add any additional meaning beyond what the schema provides (e.g., it doesn't explain parameter interactions or provide examples beyond the schema's descriptions). Baseline score of 3 is appropriate as the schema handles the heavy lifting.

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 action ('Search'), the target resource ('A2ASearch directory for AI agents, MCP servers, CLI tools and agent skills'), and the return format ('Returns name, description, type, stars, GitHub URL and capabilities for each result'). It distinguishes from sibling tools like 'get_agent' (likely fetches a single agent) and 'list_agents' (likely lists all without search) by specifying search functionality with filtering.

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 searching the directory with a query and optional filters, but does not explicitly state when to use this tool versus alternatives like 'list_agents' (e.g., for browsing vs. targeted search) or 'get_agent' (e.g., for specific agent details). It provides some context but lacks explicit guidance on exclusions or comparisons.

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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