Verify Dataset Before Relying

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

Beyond annotations (readOnly, idempotent, openWorld, non-destructive), the description discloses fail-closed receipt verification, artifact references, and the crucial limitation that it verifies published artifacts and not live upstream availability or semantic truth. It also states what inferences are permitted vs prohibited, which is substantial behavioral context.

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?

Two sentences plus a pipeline. Each sentence carries essential information: scope of use, return contents, and a critical limitation. There is no filler or repetition of schema details, and the most important 'preferred pre-trust check' is 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?

With output schema present, return values need not be spelled out. The description covers when to use, what it does, what it does not do, the allowed inferences, and the requested pipeline order. For a two-parameter tool with strong annotations, this is fully sufficient for an agent to invoke it 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?

Schema description coverage is 100%, so the parameters are already fully documented in the schema. The description adds useful high-level context (single-call, published artifacts) but does not provide per-parameter meaning beyond what the schema already states, so the baseline of 3 applies.

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 uses a specific verb and resource ('pre-trust check', 'verify before relying') and names the precise class of questions it answers: currency, staleness, unknown freshness, degraded, browser-dependent. It also distinguishes itself from a live source check and mentions the search/verify/provenance pipeline, making its role among siblings clear.

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?

Explicit when-to-use language ('preferred single-call pre-trust check', 'whenever an agent must verify before relying on data') and a clear exclusion ('not a live source check'). The pipeline 'search_datasets → verify_dataset → get_provenance' gives the agent an explicit placement relative to siblings, so no inference is needed.

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