GraphMemory-IDE MCP server exhibits significant definition quality gaps across nearly all tools. While 10 tools are explicitly defined in ide-plugins/cursor/server.ts, the actual implementation details are not visible in the provided source code (Dockerfile configs shown, but not the server.ts file). Based on the tool metadata provided: (1) Naming is inconsistent, tools like 'memory_search', 'memory_create', 'memory_update' follow verb_noun convention well, but 'graph_query', 'graph_analyze', 'knowledge_cluster', 'knowledge_insights', 'knowledge_recommend' are less action-oriented and could be more specific (e.g., 'query_graph', 'analyze_graph_structure', 'cluster_knowledge_topics'). (2) Descriptions are present but generic and short (average ~80 chars), lacking the 50-200 char LLM-optimized range. None state consequences ('modifies state'), prerequisites, or when to use this tool vs alternatives. E.g., 'memory_search' says 'Search through memories... using semantic search and filtering' but doesn't explain when this is preferred over 'graph_query' or if filters are required/optional. (3) Parameter schemas are visible but minimal: most parameters lack detailed type constraints, range bounds, or enum declarations. E.g., 'limit' appears as type 'number' with no min/max; 'filters' is a bare object with no required fields or structure. 'analysis_type' in 'graph_analyze' accepts arbitrary strings (centrality, clustering, communities, paths) but is not declared as an enum, LLMs will hallucinate invalid types. (4) No output schemas documented, cannot verify what fields are returned or their types. (5) Error handling descriptions absent, no guidance on retryability, partial failures, or next steps on error. (6) Security: no visible secrets handling, permission checks, or scope declarations. The Dockerfile shows production hardening (non-root user, security frameworks) but tool-level security is undocumented. (7) Composition: the 10 tools form a reasonable semantic space (search → create/relate → analyze/recommend), but cross-tool field naming consistency cannot be verified without output schemas.

• Add formal output schemas to every tool. Document all returned fields, their types, required fields, and examples. E.g., memory_search should return [{id: string, content: string, tags: string[], created_at: ISO8601, ...}].
• Convert free-form string parameters to enums where domain is known. E.g., analysis_type in graph_analyze should be enum ['centrality', 'clustering', 'communities', 'paths']. memory_create type should be enum ['code', 'concept', 'pattern', 'solution'].
• Add min/max bounds to all numeric parameters. E.g., limit: {type: 'number', minimum: 1, maximum: 1000}, num_clusters: {minimum: 2, maximum: 100}.
• Expand descriptions to 100-200 chars following LLM-optimization guidelines. State WHAT, WHEN, and consequences. E.g., 'memory_search: Search the knowledge graph using semantic similarity. Returns top-k most relevant memories with tags and metadata. Use this to find existing knowledge before creating duplicates. Results are ranked by relevance score.'
• Add tool annotations for risk awareness: memory_delete should have destructiveHint=true; memory_search/graph_query should have readOnlyHint=true; memory_create/memory_update should explicitly document state modification.
• Implement pagination: add next_cursor and total_count to memory_search and knowledge_recommend responses. Cap results at 20-50 items by default.
• Add error guidance: document common failure modes (not found, permission denied, invalid query syntax) with suggested next steps or alternative tools. E.g., 'If memory not found, try searching with broader query filters or list all memory types first.'