flame-mcp demonstrates solid foundational quality with 10 well-named tools that follow verb_noun conventions. All tools have descriptions (average ~180 chars, within the 10-1024 baseline). Tool annotations are correctly implemented for safety classification. However, significant gaps exist: (1) output schemas are completely undocumented, no tool declares what it returns, forcing LLMs to guess; (2) input schemas are visible in the source but sparse, only tool 1 (execute_python) and tool 3 (learn_pattern) show explicit parameter descriptions beyond the raw schema; (3) error handling is minimal, no recovery guidance or actionable error messages visible; (4) parameter constraints are missing enums where appropriate (e.g., tool 2's 'limit' accepts arbitrary integers with no bounds). The knowledge-base tools (search_flame_docs, learn_pattern, execute_python) are well-conceived for RAG-augmented code synthesis, but parameter validation and output documentation are weak. Token tracking and session stats are interesting but orthogonal to core tool quality.

• Document output schemas for all 10 tools. Example for search_flame_docs: 'Returns {"results": [{"chunk_id": string, "content": string, "relevance_score": 0-100}], "total_results": number, "cached": boolean}'. This enables LLMs to extract relevance scores and structure responses.
• Add explicit bounds to numeric parameters. search_flame_docs 'limit': add 'minimum: 1, maximum: 50' to schema; default to 5. Reset_session_stats and get_session_stats need no limit but should document typical response token counts.
• Enhance error messages with recovery guidance. When execute_python fails with a Flame API error, return: '{"error": "PySegment not found", "recovery": "Call search_flame_docs with query \'PySegment methods\' to learn the correct API. See FLAME_API.md for examples."}' instead of bare error text.
• Add pagination to list_* tools. Introduce optional 'limit' (default 20, max 100) and 'offset' parameters; return 'total_count' and 'has_more' boolean. Prevents context explosion on large workspaces.
• Implement dry-run mode for execute_python. Add optional boolean parameter 'dry_run' (default false). When true, return '{"executed": false, "parsed_code": "...", "preview": "Would create timeline named \"edit_001\""}' without modifying Flame state.
• Document tool chaining dependencies. Add to descriptions: 'Returns project_name suitable for get_timeline_info; returns library_name suitable for list_reels.' Helps LLMs plan multi-step workflows.
• Enrich list_* error responses with suggestions. If list_reels fails (library not found), return: '"Available libraries: lib_main, lib_archive, lib_temp". Call list_libraries() to discover.' This prevents dead-ends.