Sources, versions and licences

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

Annotations already declare read-only, idempotent, closed-world, non-destructive behavior, so the description does not need to restate safety. It adds valuable behavioral context by enumerating the return payload (URL, version, SHA-256, legal status, Official Journal check, CELLAR amendments) and noting the data is 'at the last refresh.'

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?

It is a single front-loaded sentence that lists the returned provenance fields without filler. The enumeration is dense but appropriate because there is no output schema to carry that 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?

With no output schema, the description must explain the return values, and it does so comprehensively for each bundled dataset. Combined with annotations that cover safety and idempotency, it gives the agent enough to call the tool correctly.

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?

There are zero input parameters, so the baseline is 4. The description does not need to explain parameters and correctly focuses on the returned data.

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 names the resource ('Where the answers come from') and enumerates the exact provenance fields returned for each bundled dataset, so the agent knows it retrieves source/version/licence metadata. It does not explicitly contrast with sibling tools like compare_origins, so it misses the top tier.

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?

There is no explicit statement of when to use this tool versus alternatives, nor any exclusions. The opening phrase implies provenance lookup, but an agent must infer the use case from the field list.

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