This server has critical gaps in definition quality. Tool descriptions are present but minimal (10-20 chars baseline). Parameter descriptions are almost entirely absent, 'steps' and 'group_trajectories' lack any explanation of expected structure or content. Input schemas exist but are severely incomplete: 'steps' is typed as array with no items schema; 'group_trajectories' items are untyped objects. No output schemas are documented. Error handling is minimal (generic 'Unknown tool' response). The server implements a specialized ML/RL domain (trajectory buffer, GRPO advantages) but fails to explain what these concepts mean or when to use each tool. An LLM cannot reliably select between add_trajectory, sample_batch, and compute_grpo_advantages without domain expertise.

• Add detailed parameter descriptions for all inputs. Example: 'steps: Array of action objects executed by the agent. Each object must have {"action": string (tool_exec|sql_query|...), "code": string, "result": string (optional)}. Minimum 1 step, maximum 100.'
• Document output schemas explicitly. For sample_batch, specify: 'Returns {"trajectories": [{"id": int, "prompt": string, "steps": array, "reward": number}], "weights": [float], "indices": [int]}'. For compute_grpo_advantages, specify: 'Returns array of floats (advantages), one per input trajectory, normalized to mean 0.'
• Expand tool descriptions to 100-150 chars with domain context. Example for add_trajectory: 'Store a completed agent reasoning trajectory (prompt, steps, final reward) for later sampling in reinforcement fine-tuning. Use this to build a replay buffer of successful and failed attempts.'
• Add input validation with actionable errors. Example: 'batch_size must be 1-100 (got 500)' instead of silent failure. Validate that group_trajectories is non-empty and each item has a "reward" field.
• Add a discovery tool or documentation link explaining the RL/GRPO domain. LLMs need context: What is a trajectory? When do I call sample_batch vs add_trajectory? What do advantages represent? Consider a 'describe_buffer_state' tool that returns buffer size, reward statistics, and usage guidance.