This server has a single tool with critical definition gaps. The tool name 'track_ai_visibility' lacks a clear action verb (should be 'get_ai_visibility' or 'track_brand_mentions'). The description is present but generic (78 chars, below the 194-char baseline for A+ tools). Input parameters lack type definitions in the visible schema, 'target_queries' is declared as array but has no item type specification. No output schema is documented; the response structure is inferred from example_usage.py only. No error handling, validation, or recovery guidance. The implementation is a mock returning hardcoded data, not a real integration. No MCP server scaffolding is visible, this appears to be a standalone Python class, not an MCP-compliant tool server.

• Implement a full MCP server using the official Python SDK (mcp package). Register tools via server.tool() decorator with complete schemas, descriptions, and input validation.
• Rename tool to 'get_ai_visibility' and expand description to 50-150 chars explaining: WHAT (retrieves brand mention rates across LLM platforms), WHEN (use to benchmark brand visibility), WHAT IT RETURNS (visibility score, per-model breakdown, competitor share).
• Add complete input schema: brand_name (string, required, 1-255 chars), target_queries (array of strings, required, 1-10 items, each 5-200 chars). Include enum or pattern constraints if only certain query types are valid.
• Document output schema with typed fields: visibility_score_pct (number 0-100), model_breakdown (object with model names as keys, mention rates as values), competitor_share_pct (number 0-100), timestamp (ISO 8601 string).
• Add error handling: return structured errors for invalid brand names ('Brand not found. Try: GenPark, GenPark AI, GenPark Visibility'), network timeouts, and unsupported query types. Include actionable recovery steps.