The claudewatch server exposes 5 analytics tools with basic descriptions and sparse parameter documentation. Most tools lack input schema clarity, parameter descriptions are minimal or absent, and output schemas are not documented. Tool naming follows verb conventions (get_*), but the overall definition quality is below production standards. No evidence of error handling guidance, and schema validation is minimal.

• Document the output schema for each tool. For get_agent_performance, include fields returned (e.g., sessions array with fields: session_id, tool_name, call_count, avg_tokens). Use JSON Schema or a detailed description.
• Add pagination parameters (limit, offset or next_cursor) to get_agent_performance and get_effectiveness. Document the default and maximum result size (e.g., 'Default limit: 20, max: 100').
• Enhance parameter descriptions for get_project_anomalies: add range guidance for threshold (e.g., 'Z-score threshold (1.0 - 4.0, default 2.0)'). Explain what each threshold implies (e.g., '2.0 ≈ 95% confidence').
• Add error handling hints. For tools that filter by session_id or project, explain: 'If session not found, call get_agent_performance() to list available sessions.' For anomaly detection, specify: 'Requires at least 3 sessions; returns empty if threshold not met.'
• Clarify the relationship between tools. Add a brief guide to the server description: 'Start with get_agent_performance for a macro view, drill into get_project_anomalies for outliers, then get_cost_attribution to understand the cost drivers.'
• For get_context_pressure, document the meaning of each status (comfortable/filling/pressure/critical), e.g., 'critical ≥ 85% utilization, pressure 70 - 85%, filling 50 - 70%, comfortable < 50%.'
• Consider adding rate-limiting or async hints in descriptions if tools analyze large datasets. Let LLMs know if operations may be slow.