This server has critical definition gaps. All 4 tools lack proper parameter descriptions, inputs are documented only as 'Dictionary of...' or 'Current parameter weights' without explaining what values are valid, expected ranges, or formats. No output schemas are documented; responses are inferred from code (e.g., {"status": "task_started"}). Tool names are verb-based but descriptions are domain-specific jargon ('path integral', 'Omega_k') that does not explain WHEN or WHY an LLM should call them. No error handling guidance. No input validation rules. Schemas are present but minimal (only type:object, no properties). Per-tool average: 28/100.

• Expand parameter descriptions to 50 - 150 characters each. For 'initial_weights', write: 'Dictionary mapping parameter names (strings) to float values (e.g., {"w1": 0.5, "w2": -0.3}). All keys must be present in subsequent calls.' This tells LLMs the expected structure and constraints.
• Document output schemas explicitly. Add to tool definitions: 'Returns {"status": "task_started"}' for start_task, '{"running_integral": {<param_name>: float}}' for update_step, etc. Include field types and meanings.
• Rewrite tool descriptions to explain domain-agnostic intent. Instead of 'Accumulate small parameter changes multiplied by gradient via path integral', write: 'Track how much each parameter contributes to learning on the current task. Call this after each optimization step with the updated weights and gradients. Returns running importance scores.'
• Add input validation and error messages. If 'initial_weights' is empty or contains non-numeric values, return {"error": "initial_weights must be a non-empty dict with string keys and numeric values"}. This guides LLM recovery.
• Define input schemas with explicit properties. For start_task: {"type": "object", "properties": {"initial_weights": {"type": "object", "additionalProperties": {"type": "number"}, "description": "..."}}}, "required": ["initial_weights"]}