This server has 9 tools with mixed quality. Three domain tools (hybrid_retrieve_precedents, detect_graph_conflicts, deep_research) have substantive descriptions and reasonable parameter schemas, but lack output schema documentation. Six infrastructure/health tools (health_check, readiness_check, get_server_metadata, search, fetch, list_upstream_servers) are minimal, mostly READ_ONLY utilities with sparse parameter descriptions. No tool has documented return types or examples. Parameter constraints are missing (e.g., num_results defaults to 5 but no max is stated). Several parameters lack descriptions entirely (e.g., 'thread_id' and 'step_id' appear in multiple tools but are not well explained). Error handling guidance is absent. The server follows basic naming conventions (verb_noun pattern) and includes tool annotations (toolAnnotations=true), but falls short of production-grade definition quality.

• Document return/output schemas for all tools. At minimum, list the fields in the response (e.g., hybrid_retrieve_precedents returns: [{ precedent_id, clause_text, similarity_score, obligation_chain }]). This enables LLM chaining.
• Add min/max constraints to numeric parameters: num_results (1 - 50), max_concurrent_research_units (1 - 10 is present, good), limit (1 - 100), offset (0+). Include these in both the schema and description.
• Clarify 'thread_id' and 'step_id' in affected tools. Explain: 'thread_id: Unique identifier for the conversation thread (passed by the client for idempotency tracking)' and 'step_id: The plan step identifier provided by the agent orchestrator'.
• Expand infrastructure tool descriptions to 100+ chars explaining PURPOSE, not just output. E.g., 'health_check: Returns MCP server connectivity status (response time, dependencies online). Call this first if the client detects timeouts, or periodically in monitoring dashboards.'
• Add error handling guidance. E.g., hybrid_retrieve_precedents: 'If no precedents found, try: (1) broaden the query (remove specific terms like country names), (2) call search with a related keyword first to validate the knowledge graph, (3) check user_id matches the current session.'
• Document the structure of search/fetch results. Specify: 'search returns: [{ id, name, type, description, upstream_server_id }]' and 'fetch returns: { id, name, type, description, status, last_checked_at }'.
• Add dependency hints to tools that call external services (deep_research → Tavily, hybrid_retrieve_precedents → Neo4j + pgvector). E.g., 'Requires Tavily API configured; falls back gracefully if unavailable.'