Single tool 'get_docs' has a reasonable name and basic description, but critical gaps undermine quality. Description is 106 chars (within 10-1024 range) but lacks WHEN to use it and prerequisites. Input schema is present with two string parameters, but both lack type constraints (no enums for 'library' despite supporting only 3 values). Output is unstructured text with no schema documentation. No error handling guidance, ValueError for unsupported library is raised but not documented. Tool does not follow composition patterns: it combines search + fetch + parse into one call, which is reasonable, but the response is raw concatenated text rather than structured results. No pagination despite potentially returning large documents. Parameters lack format/constraint descriptions (e.g., 'library' should enumerate langchain|openai|llama-index). Baseline: avg tool description 194 chars (this is 106), avg params per tool 4 (this has 2), 100% of A+ tools have documented return types (this has none).

• Add enum constraint to 'library' parameter: {type: 'string', enum: ['langchain', 'openai', 'llama-index'], description: 'The documentation library to search in'}
• Document output schema in docstring: 'Returns: List of {url (str), title (str), excerpt (str, first 500 chars)} objects, or 'No Results Found' if no matches'
• Expand description to 150-200 chars: 'Search documentation for langchain, openai, or llama-index. Requires SERPER_API_KEY environment variable. Returns up to 2 pages of results with extracted text. Use when user asks about library features or API usage.'
• Add parameter descriptions: query: 'Search term (e.g., "Chroma DB", "vector store"). Will be scoped to selected library domain.'; library: 'Documentation library: langchain, openai, or llama-index'
• Implement result capping and pagination: return structured {results: [{url, title, excerpt}], total_results: int, has_more: bool} instead of raw concatenated text