Three tools with basic verb-noun naming (search_, list_, download_). All have descriptions (10-60 chars, below baseline 194 chars). Input schemas present with types and descriptions. However, descriptions are minimal and lack context for LLM selection. No output schema documentation. No error handling guidance. Parameter descriptions are sparse. Tool definitions inferred from test file and skill scripts rather than explicit MCP registration, cannot verify actual tool registration mechanism.

• Expand tool descriptions to 100-200 chars. Example: 'Search the Gazebo Fuel repository for Google Scanned Objects by name or category. Returns up to 100 results with metadata (filesize, license, download count). Use this to discover available models before downloading.'
• Document output schemas formally. For search_google_scanned_objects, specify: returns {assets: [{name, description, categories, filesize, downloads, license_name, license_url, thumbnail_url}], count, license}.
• Add parameter constraints to schema. For limit, set minimum: 1, maximum: 100, default: 20. For query, add pattern or enum if only certain search types are valid.
• Implement error recovery guidance. When download fails, return: 'Failed to download model_name: [reason]. Verify the model exists via search_google_scanned_objects(). Check disk space and network connectivity.'
• Verify and document MCP transport. Confirm whether this server uses HTTP, SSE, or STDIO. Provide explicit tool registration code showing how tools are exposed to MCP clients.