Single tool 'synthesize_test_suite' has a basic schema but critical gaps in parameter descriptions and output documentation. The tool name is clear (verb_noun pattern), but parameter descriptions are minimal or absent. The 'param_schema' parameter lacks guidance on expected format and structure. No output schema is documented, LLMs cannot predict what fields the response contains. Error handling is absent. The tool definition is explicitly visible in mcp_server.py, but quality falls well below production baseline (194 chars avg description, 72 chars avg param annotation).

• Add a detailed description to 'param_schema' parameter: 'A dict mapping parameter names to type hint strings. E.g. {"amount": "float", "user_id": "str", "items": "list"}. Supported types: int, float, str, list, dict, bool, object, array, number, string, mapping, boolean.'
• Expand tool description to 150 - 250 chars explaining purpose and use case: 'Generates diverse edge-case and boundary test inputs for tool parameters to uncover execution failures. Use this to systematically test tool robustness before deployment. Returns a list of test case dicts, each with parameter values set to boundary conditions (empty, null, overflow, injection payloads).'
• Document the output schema in the tool description or as a separate field: 'Returns a list of dicts, each representing one test case. Each dict has the same keys as param_schema, with values set to boundary/edge-case candidates (e.g., empty strings, negative numbers, special characters, very large values).'
• Add description to 'max_tests': 'Maximum number of unique test cases to generate (1 - 100). Default 10. Higher values produce more comprehensive coverage but longer execution time.'
• Implement error handling: validate param_schema is a non-empty dict, max_tests is 1 - 100, and return actionable errors like 'param_schema must be a non-empty dict mapping parameter names to type strings' if validation fails.