validate_data

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

The description adds a billing-relevant behavior not present in annotations: 'Costs API credits per call.' This is valuable operational context on top of the readOnlyHint and destructiveHint fields. There is no contradiction; validating data is consistent with a read-only mutation profile.

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?

Three short sentences with no filler: purpose, cost warning, and prerequisite are each clearly separated. The most important operational cautions are front-loaded.

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?

For a validation tool with no output schema, the description explains the core operation, the prerequisite discovery step, and the credit cost, which are the most important missing pieces. It could go further by describing the validation response shape or error behavior, but the current coverage is strong for the tool's complexity.

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 schema already documents all five parameters. The description adds some extra meaning by emphasizing that standards and versions come from list_standards, but it does not elaborate beyond that baseline.

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 a specific action ('Validate a data payload') against a named resource ('supported sustainability standard') and adds the Validation-as-a-Service framing. It is not a tautology and reads distinctly from list_standards, though it does not explicitly differentiate itself from the sibling ofp_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?

The description gives concrete usage context by telling the agent to call list_standards first to learn supported standards and versions. It implies when this tool is appropriate, but it does not state exclusions or directly compare with sibling validation-related tools.

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