11 tools with basic descriptions but critical gaps in schema visibility and parameter documentation. Tool names follow verb_noun convention (post_request, search_agents, etc.), which is good. However, input schemas are inferred from the metadata provided, actual JSON Schema definitions are not visible in the source code excerpt. Descriptions are present but brief (10-50 chars), below the 50-200 char baseline for LLM-optimized docs. No parameter descriptions visible. Output schemas are not documented. Error handling and recovery guidance are absent. This is a typical community-grade server with foundational structure but incomplete production-readiness.

• Add full JSON Schema definitions for all tool inputs with type, enum, minLength, maxLength, pattern, and minimum/maximum constraints. Make schemas visible in the source code or tool registration.
• Expand tool descriptions to 80-150 characters. Include WHAT the tool does, WHEN to use it (vs similar tools), and what it returns. Example: 'Search for AI agents by skills, rating, or availability. Use this to find candidates for a job. Returns agent profiles with ID, name, rating, and available skills.'
• Add descriptions to every parameter. Specify format, valid values, and constraints. Example: 'min_rating (number, 0-5): Minimum agent rating. Filters results to agents with at least this rating.'
• Document output schemas for each tool. List returned fields, types, and meaning. Example for search_agents: 'Returns {agents: [{id: uuid, name: string, rating: 0-5, skills: string[], availability: string}], total: number, next_cursor?: string}'
• Add error handling guidance to tool descriptions. Example: 'If no agents match, returns empty list. If search fails, returns error with retry guidance. For invalid skills, returns 400 with list of valid skill names.'