Two tools with minimal schema documentation and generic descriptions. Both tools accept only a single 'query' string parameter with no type constraints, enums, or validation guidance. Descriptions are present but lack actionable context (10 - 20 chars above the 20-char floor). No output schemas documented. No error handling guidance. No parameter constraints or format specifications. Tool composition is reasonable (search_the_web and hybrid_graph_vector_search are distinct), but definitions fall well below production baseline (194 chars avg description, 100% param documentation in A+ tools).

• Add explicit output schema documentation for both tools. Example: search_the_web returns {results: [{title: string, url: string, snippet: string}], total_count: number}. Document all fields the LLM will receive.
• Expand 'query' parameter description to specify format, length constraints, and examples. Example: 'Search query string (1 - 500 characters). Supports natural language and boolean operators (AND, OR, NOT). Example: "machine learning frameworks 2024"'.
• Rewrite tool descriptions to answer: What does it do? When should I call it? What does it return? Example for search_the_web: 'Search the live internet for current information, news, and web pages. Use this when you need real-time data not in your knowledge base. Returns up to 10 results with title, URL, and snippet.'
• Add error handling guidance in descriptions. Example: 'If the search times out (>15s), retry with a simpler query. If no results found, suggest alternative search terms to the user.'
• Document pagination and result limits. Example: 'Returns up to 10 results per query. Use the offset parameter to paginate through larger result sets. Limit queries to 3 per minute to avoid rate limiting.'

Inferred effective spec: 2026-07-28+.

• Relies on Logging (deprecated) - log to stderr or use OpenTelemetry