The Sibyl MCP server defines 4 tools with basic descriptions and parameter schemas, but falls significantly short of production-grade quality. Tool descriptions are present but lack the specificity needed for LLM decision-making (averaging ~100 chars, below the 194-char baseline for A+ tools). Parameter descriptions exist but are minimal and do not explain constraints, formats, or when to use related tools. Output schemas are not documented in the source code, only inferred from docstrings. No error handling guidance is visible. The analyze_model tool appears to be a composite tool without clear indication of output structure or error recovery paths. No input validation, constraint documentation, or recovery suggestions are evident. The server shows infrastructure maturity (Docker, health checks) but tool definitions lack the rigor required for reliable agent integration.

• Document formal output schemas for each tool. Example for search_models: {type: 'object', properties: {results: {type: 'array', items: {type: 'object', properties: {model_id: string, name: string, score: number}}}, total_count: integer, next_cursor?: string}}
• Extend tool descriptions to 100-200 characters with LLM selection logic. Example: 'Search ExampleDomain models by semantic similarity using vector embeddings. Use this to find models matching a concept or description. Returns up to top_k results ranked by similarity score. If no results, try find_patterns to locate similar error patterns or logic issues.'
• Add constraint documentation to parameters. Example for query: 'Search query (natural language, 1-500 chars, case-insensitive). Examples: "payment processing", "user authentication". For regex patterns, use pattern_type filter instead.'
• Define valid enum values for pattern_type in find_patterns description: 'Pattern type filter (optional): sql_error (database/query issues), schema (data structure problems), logic (algorithmic errors), performance (optimization opportunities).'
• Add range constraints to numeric parameters: top_k description should state '(1-100, default 10)', limit description should state '(1-100, default 10).'
• Add error recovery guidance. Example: 'If no results found, try broadening the query or checking spelling. If semantic search fails, try keyword search or browse model categories.'
• Clarify analyze_model composition. Describe: 'Runs comprehensive analysis by combining get_model_info (dependencies, metadata), search_models (related models), and find_patterns (similar issues). Returns structured result {info: {...}, related_models: [...], patterns: [...]}. Use this for detailed exploration; use individual tools for targeted queries.'