This MCP server has 2 tools with minimal quality standards met. Both tools have descriptions and basic input schemas, but lack rigor in several critical dimensions. Tool names are action-oriented (retrieval, search), which is good. However, parameter descriptions are extremely sparse (single short lines), output schemas are undocumented, and error handling is minimal. The server initializes a stateful FAQ engine at startup (faq_engine global), which violates stateless request principles. No per-tool quality gate or validation guidance. This is a typical community project with working basic functionality but insufficient production hardening.

• Document the output schema for each tool. For python_faq_retrieval_tool, specify if the returned string is plain text, markdown, JSON, or structured with source citations. For firecrawl_web_search_tool, define the structure of list items (URL, title, snippet, relevance score, etc.) and include a 'total_results' or 'next_cursor' field for pagination.
• Expand parameter descriptions to 50 - 150 characters. Example for 'query': 'A natural-language search query. Be specific: instead of "python", use "python list vs tuple differences" to reduce irrelevant results.'
• Add result limiting guidance. E.g., 'Returns up to 20 most relevant results. Use pagination tokens if available to fetch additional results.'
• Clarify tool selection strategy in descriptions. Example: 'Use this tool FIRST if the question relates to Python syntax, data structures, or standard library. Fall back to web search if FAQ returns no relevant results or user asks about third-party libraries.'
• Implement actionable error responses. Example: 'Firecrawl API returned 429 Too Many Requests. Retry after 60 seconds. If this persists, the API key may have exceeded quota, check https://firecrawl.dev/dashboard.'
• Add idempotent and read-only hints to tool definitions (structured annotations) to inform agents that these tools are safe to retry and do not modify state.
• Shorten tool names to verb_noun pattern: 'query_python_faq' and 'search_web'. This aligns with the 18-character baseline and makes intent clearer.
• Document whether the FAQ engine pre-loads all embeddings on startup or loads them on-demand. If pre-loading, specify memory footprint and startup latency. If on-demand, document per-query latency expectations.