Single tool 'retrieve_tutorials' has a clear action verb and reasonable description (127 chars, within 10-1024 range). Input schema is present with three parameters, all typed and described. However, output schema is not documented in the source code, critical for LLM planning. Parameter 'tool_name' is optional but lacks enum constraints or format guidance. No error handling guidance visible. The tool is read-only (good), but lacks pagination support despite potentially returning multiple results. Description could be more specific about result structure and when to use this tool vs. alternatives.

• Document the output schema explicitly: e.g., 'Returns an array of objects with fields: {tutorial_id (string), title (string), content (string), tool_name (string), relevance_score (float 0-1), metadata (object)}'. This enables LLMs to extract and chain results.
• Add pagination: introduce 'offset' (default 0) and 'limit' (default 5, max 50) parameters. Return 'total_count' and 'has_more' in response. Document in description: 'Results are paginated; use offset to iterate through large result sets.'
• Constrain 'tool_name' with an enum or discovery mechanism. Either list valid tool names in the description, or add a separate 'list_available_tools' tool that returns valid names for filtering.
• Add error handling guidance: 'If no results found, try a broader query or omit tool_name filter. If tool_name is invalid, call list_available_tools() first.' Include this in the description.
• Clarify the semantic search behavior: 'Uses vector embeddings to find tutorials semantically similar to your query, not just keyword matches. Best for conceptual searches like "how to train a model" rather than exact tool names.'

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

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