This server exhibits fundamental structural and quality issues that make it unsuitable for production. The most critical problems: (1) Duplicate tool definitions, search_papers and extract_info appear three times each across mpc_0, mpc_1, and mpc_2, violating the single-responsibility principle and creating confusion about which implementation is canonical. (2) Tool descriptions are vague and generic; they lack actionable guidance for LLM selection. (3) Input schemas are present but minimally documented, parameter descriptions are brief and lack constraints (min/max for max_results, format hints for topic). (4) No output schema documentation, what does search_papers actually return? A list of strings? Objects with metadata? This forces LLMs to guess. (5) No error handling guidance, what happens if arXiv is unreachable? If a paper_id is not found? (6) Tools like get_available_folders, get_topic_papers, generate_search_prompt exist in mpc_3/research_server.py but their full implementations are not visible in the source provided, preventing verification of schema completeness. (7) The weather tools (get_weather, weather_resource, weather_report) in mcp_gradio_server/simple_server.py are contextually disconnected from the research paper domain, suggesting incomplete refactoring or test code left in production.

• Consolidate duplicate search_papers and extract_info definitions into a single canonical implementation in one module (e.g., mcp_chatbot/research_server.py). Remove mpc_0, mpc_1, mpc_2 modules or clearly document their distinct purposes.
• For each, answer: WHAT does it do? WHEN should the LLM call it? What are PRECONDITIONS? Example: 'search_papers: Search arXiv by topic and save results locally. Call this first to retrieve a set of relevant papers; use extract_info afterward to get full details on a specific paper_id. topic is a free-text search query (e.g., "machine learning safety"); max_results (1-100, default 5) controls result count. Returns a list of paper IDs suitable for extract_info calls.'
• Document all return schemas explicitly. For search_papers, state: 'Returns: List[str] of arxiv paper IDs (e.g., ["2401.12345", "2312.54321"]).' For extract_info, return JSON: {"found": true, "paper_id": string, "title": string, "authors": List[string], "summary": string, "pdf_url": string, "published": string (ISO 8601 date)} or {"found": false, "error": string, "suggestion": string (if available)}.
• Add parameter constraints to all tools: max_results (min=1, max=100), topic (required, non-empty string), paper_id (required, format: arxiv ID e.g. '2401.12345'), location (required, accepts city name or coordinates). Include these in parameter descriptions, not just defaults.
• Implement explicit error handling with actionable recovery guidance. Examples: 'If arXiv search fails due to network timeout, try again in 30 seconds.' 'If paper_id not found, use search_papers to find related papers by topic.' 'If location format is invalid, provide city name and state (e.g., "San Francisco, CA").'