Single tool 'get_docs' has a reasonable description (194 chars) and basic parameter documentation, but critical gaps prevent higher scoring. Parameters lack type constraints (no enums for 'library' despite supporting only 3 values). Output schema is completely undocumented, LLMs cannot plan downstream actions. Error handling is minimal (ValueError for unsupported library, generic 'No results found'). No guidance on what to do if search fails or timeout occurs. Tool returns raw concatenated text from multiple URLs with no structure, pagination, or field documentation. Naming is acceptable (verb_noun pattern) but the tool conflates search + fetch operations.

• Add enum constraint to 'library' parameter: {"type": "string", "enum": ["langchain", "llama-index", "openai"], "description": "The documentation library to search in"}
• Document output schema explicitly: return structured object with fields like {"results": [{"title": string, "url": string, "snippet": string, "source_library": string}], "total_results": number, "search_query": string}
• Add actionable error messages: 'Library not supported. Available: langchain, llama-index, openai' and 'No documentation found for query. Try broader search terms or check library name.'
• Split into two tools: search_docs (returns URLs + snippets, fast) and fetch_doc_content (retrieves full page, slower). Allows agent to preview before fetching.
• Add result limiting and pagination: cap to 5 results max, return next_cursor if more available. Document in description: 'Returns up to 5 documentation pages per query.'