Six tools with basic definitions but significant quality gaps. All tools have descriptions (10-50 chars, below the 50-200 char LLM-optimized baseline), and input schemas are present but minimal. Parameters lack detailed descriptions explaining constraints, formats, or dependencies. No error handling guidance, no output schema documentation, and no tool annotations (readOnlyHint, etc.). Tools are simple mock implementations suitable for examples but not production-grade. Naming is verb-based (search, get_fun_fact, list_available_topics, generate_completion) which is correct, but descriptions are too terse to guide LLM selection effectively.

• Expand tool descriptions to 50-200 chars. Example: 'search' → 'Search the web for information on a topic. Use this for current events, facts, or general knowledge. Returns a list of results with titles and snippets.' This tells LLMs when to call it vs. quick_facts (which returns pre-curated facts) or get_fun_fact (which returns trivia).
• Add parameter descriptions with constraints. Example: 'query' → 'The search query (1-500 characters, alphanumeric and spaces). Avoid special characters or operators.' This prevents invalid input and guides LLM behavior.
• Document output schemas. Example: search returns {results: [{title: string, snippet: string, url: string}], total_count: integer}. This enables LLMs to extract and chain data.
• Add error handling guidance. Example: 'If no results found, try a simpler query or use quick_facts for common topics.' This tells LLMs how to recover from failures.
• Add tool annotations to the schema. Mark all tools with readOnlyHint: true since they do not modify state. This enables agents to reason about safety and retry behavior.

Inferred effective spec: <=2025-11-25.

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