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

Annotations already declare readOnly, idempotent, non-destructive, and closed-world. The description adds genuine behavioral context beyond them: no network fetch occurs, and each row carries the vendor page the summary was checked against, which tells the agent what kind of data comes back.

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, purpose front-loaded, then return shape, then constraint. Each sentence earns its place; the middle sentence about the vendor page is slightly awkwardly worded but still informative.

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 zero-parameter, read-only list tool with no output schema, the description covers what it returns (crawler tokens, one per row with a vendor reference) and what it will not do (fetch). Sufficient for correct invocation, though more detail on row fields is absent.

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?

With zero parameters the baseline is 4, and the description adds useful value by explicitly forbidding invented arguments (url, host, target, website, robots_url, fetch) that an agent might otherwise hallucinate for a crawler-related tool.

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?

States a specific verb and resource ('List the AI crawler robots.txt tokens in this dataset') and scopes it to the local dataset, which implicitly separates it from the fetching siblings. It does not name check_robots or get_crawler explicitly, so the differentiation is left partly to inference.

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?

'This tool does not fetch any website' plus the explicit do-not-send list clearly signals the usage context: read a static local list rather than probe a URL. The alternative (check_robots) is only implied, not named, so the routing guidance is good but not airtight.

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