Search documentation for langchain, openai, and llama-index libraries using DuckDuckGo and Google
Single tool 'get_docs' has a reasonable name and basic description, but critical gaps in parameter descriptions, output schema documentation, and error handling severely limit production readiness. The tool lacks input validation guidance, does not document expected output structure, and provides no recovery guidance for common failure modes (network timeouts, no results found). Parameter descriptions are minimal (10-15 chars each) and lack format/constraint information. No pagination support despite potentially returning large text blocks. Error responses are generic ('No results found', 'Timeout error') without actionable guidance.
Search the latest docs for a given query and library. Supports langchain, openai, and llama-index.
Parameter descriptions are under 20 characters and lack format/constraint information. 'query' description is 35 chars but generic; 'library' description mentions examples (langchain, openai, llama-index) but does not declare enum constraint or explain what happens if unsupported library is passed.
No output schema documented. Tool returns raw text string from web content without specifying structure, format, length limits, or how pagination would work. LLM cannot plan downstream processing or know when content is truncated.
Error handling is generic and non-actionable. 'No results found' does not guide LLM on retry strategy, query reformulation, or alternative approaches. 'Timeout error' lacks retry guidance. ValueError on unsupported library is acceptable but not wrapped in recovery guidance.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 42 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 39 | - | v1 |
No input validation or constraint documentation. Parameter 'library' accepts any string but only langchain/openai/llama-index are valid. Should declare enum constraint and validate early with actionable error message ('Invalid library: got "pytorch", must be one of: langchain, openai, llama-index').
No pagination or result limit control. Tool concatenates content from multiple URLs into unbounded text block. For verbose documentation (e.g., LangChain docs), this could easily exceed token budgets. No max_results or limit parameter exposed.
Web scraping implementation (Google + DuckDuckGo fallback) is fragile and unmaintained. No User-Agent rotation, no retry logic on transient failures, no circuit breaker for rate limiting. Tool will silently fail if Google/DuckDuckGo HTML structure changes or blocks requests.
No timeout error guidance. fetch_url() returns 'Timeout error' string on timeout, LLM does not know if it should retry, adjust query, or try a different library. Should categorize as retryable and suggest retry with backoff.