Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already establish safety (read-only, idempotent, non-destructive). The description adds the behavioral logic of the validation checks, which is beyond the annotations. It does not discuss error handling, but the presence of an output schema offsets that need.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with the core function, followed by context. No redundant information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is well-described for its complexity. The output schema and annotations cover return values and safety. The description covers validations performed and target platforms. Minor ambiguity about what 'expected schema' means relative to the required/optional args format, but it's adequately complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description's reference to 'arguments match expected schema' adds little beyond the schema's own descriptions. It does not clarify the allowed_functions structure beyond what's in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb 'Validate' with a clear resource 'LLM function call / tool_use output', and enumerates the exact validation checks (name in allowed list, arguments schema, extra/missing args). This clearly distinguishes it from generic validators like llm_output_validator or json_schema_validate.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It states the use case: 'For OpenAI function calling & MCP tool_use testing.' This provides clear context for when to use the tool. It does not explicitly name alternatives or when not to use it, but the targeted scope is clear enough.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.