Two tools with minimal descriptions (both under 100 chars) and incomplete parameter documentation. validate_code_ast has a single string parameter with basic description; verify_skill_execution has three parameters, one typed as 'any' with no constraints. No output schemas documented. No error handling guidance. Tool names are verb-based but descriptions lack context on WHEN to use them, WHAT they return, or recovery paths. No parameter constraints (enums, ranges, formats). Schemas present but sparse, missing type info for 'expected_result' and no output structure defined.

• Document output schemas for both tools. Example: validate_code_ast returns {valid: bool, error?: string, function_name?: string, has_docstring: bool, has_return: bool, arg_count: int}. This lets LLMs know what fields to expect.
• Constrain 'expected_result' parameter: add enum or pattern. If it accepts any JSON value, state that explicitly. If it must be numeric, string, or boolean, declare the type and provide format examples.
• Expand tool descriptions to 100 - 200 chars. Include: (1) What it does, (2) When to use it, (3) What it returns. Example: 'Validates Python code syntax and structure (docstrings, return statements, function definitions). Use before executing synthesized skills. Returns validation status and function metadata.'
• Add error classification to verify_skill_execution description: 'Returns APPROVED (passed), FAILED_ASSERTION (test mismatch, retry with different code), RUNTIME_ERROR (execution failed, check code), or REJECTED (syntax invalid, see validate_code_ast first).'
• Add parameter constraints: code_str should specify max length (e.g., 100KB), Python version (3.9+), and what syntax is accepted. test_call_expr should document that it must be a valid Python expression that can be eval()'d in the code's namespace.