dataset_status

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

No annotations are provided, so the description carries the full burden. It discloses what the tool reports (coverage and freshness metrics) and implies read-only behavior since it merely reports status. But it doesn't state that this is a read-only operation, doesn't mention any auth requirements or side effects, and doesn't describe the return shape or whether it aggregates data in real-time. It's partially transparent but leaves gaps.

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 a single compact sentence that lists the key metrics in a clear, scannable way. It front-loads the core purpose ('Coverage and freshness of the underlying dataset') and then enumerates specifics. Slightly dense with the colon-list, but efficient and earns its place.

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 reporting tool, the description covers the essential context: what data it reports. It doesn't explain the return format or any caveats about how metrics are computed, but with no output schema, the agent might need more detail on response structure. Given the tool's simplicity, this is adequate but not 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?

This tool has zero parameters, so the description doesn't need to explain parameter meanings. The description focuses entirely on what the tool returns, which is appropriate. Baseline is 4 for 0-param tools, and the description adds value by detailing the reported metrics.

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 that this tool reports the coverage and freshness of the underlying dataset, and enumerates the specific metrics it returns: vendors mapped, machine-readable feeds, incidents on record, recent incidents, platform breakdown, poll cadence, license, and bulk downloads. This distinguishes it from sibling tools like vendor_status and vendor_incidents, which are vendor-specific rather than dataset-wide. However, it doesn't explicitly name the sibling it is not, so it loses one point.

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 when to use this tool: when you need dataset-wide coverage and freshness metrics, rather than per-vendor status. But it doesn't explicitly state when not to use it, or name alternatives like vendor_status or vendor_incidents. Usage guidance is present but implied, not explicit.

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