validate_output

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

No annotations are provided, so the description must carry the full burden. It lists checks but does not disclose whether the tool is read-only, requires authentication, or has side effects. It does not describe the output format or error behavior.

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?

The description is concise, with the main purpose stated first, followed by a bullet list of checks. Every sentence adds value without unnecessary words.

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?

Given the tool has only one parameter and uses an output schema, the description covers the key aspects: what the tool does, what it checks, and the parameter's dual nature. It is sufficient for a single-parameter validation tool.

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?

The schema has 0% description coverage, but the description adds meaning by clarifying that yaml_content can be either actual YAML content or a file path. This goes beyond the schema's type definition.

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 clearly states the tool validates Gateway API YAML against schema rules and AWS LBC constraints, listing specific checks like required fields, L4/L7 mixing, etc. This distinguishes it from siblings such as analyze_ingress or convert_to_gateway_api.

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?

The description implies usage for validation but does not explicitly state when to use this tool versus alternatives. No exclusions or when-not-to-use guidance is provided; the sibling tools are not compared.

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