Translation Helps MCP demonstrates weak definition quality across most tools. While 7 of 10 tools have descriptions and explicit schemas are partially visible, the descriptions are often generic, parameters lack depth, and error handling guidance is absent. The server mixes production tools (fetch_scripture, fetch_translation_notes) with three experimental tools marked with 🧪 that return mock data, a significant red flag for production use. Schema details are inferred from test files rather than visible in a canonical tool registration block, limiting confidence in completeness. The few tools with detailed schemas (fetch_scripture) show competent structure, but most lack output schema documentation, parameter validation guidance, and error recovery hints. This is a typical C-grade community MCP server.

• Remove or gate experimental tools (ai_summarize_content, ai_quality_check, smart_recommendations, cache_analytics) behind a feature flag. Document that they return mock data and are not for production use. Alternatively, implement real backing logic or remove them entirely.
• Expand tool descriptions to 100-200 characters. For each fetch_scripture variant, explain: (1) What it returns (text, metadata, or structure), (2) WHEN to use it (e.g., 'Use fetch_translation_notes before fetch_translation_questions to understand context'), (3) What makes it different from related tools.
• Document output schemas for all tools. For fetch_scripture, document that it returns {text, language, version, organization, metadata}. For fetch_translation_notes, document the structure of notes (array of {verse, note_text, complexity}). This allows LLMs to chain tools correctly.
• Add parameter validation guidance. E.g., 'reference must be a valid Bible reference (Book Chapter:Verse format, e.g., Romans 1:1, John 3:16-20). If invalid, returns an error with suggested corrections.'
• Implement error recovery hints. E.g., 'If reference is not found, try searching with a partial book name: search_books(partial_name=reference).' Provide fallback suggestions in error responses.
• Rename experimental tools to use _experimental suffix and mark them clearly unavailable in the tool list. E.g., 'get_ai_summary_experimental' with description 'NOT YET IMPLEMENTED, returns mock data for testing. Do not use in production.'
• Add per-tool idempotency and side-effect documentation. All current tools are READ_ONLY, which is good, explicitly state this in descriptions. E.g., 'This tool only reads data and has no side effects. Safe to call multiple times.'