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

Beyond the readOnly, openWorld, idempotent, and non-destructive annotations, the description discloses crucial runtime behavior: it reads precomputed pipeline evidence rather than live sources, evidence_available=false semantics, the one-request-per-second rate limit with burst, and the DataPulse read-only/no-API-key property. It also warns against inferring current source reachability or truth.

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 long but every sentence carries useful information: field list, workflow sequence, exclusions, caveats, and rate limiting. It is front-loaded with the core purpose and then adds necessary operational detail. Slight redundancy around 'use it for a complete published receipt' and the earlier audit phrasing prevents a perfect 5.

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?

Given the complex sibling landscape and the need to route agents to correct verification tools, the description covers purpose, alternatives, workflow, behavioral caveats, rate limits, and failure semantics. The presence of an output schema and full annotations further reduces the need to document return values or safety, so nothing essential is missing.

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 coverage is 100% and the dataset_id parameter is already well described in the schema, including 'stable dataset slug returned by search_datasets' and the requirement to use a slug rather than a display name. The tool description adds little beyond the schema's parameter documentation, so the baseline 3 is appropriate.

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 states a precise verb and resource: it retrieves the complete published evidence receipt for one dataset and lists the exact fields included. It also distinguishes itself from sibling verification tools by explicitly naming what it is not for, making its purpose unambiguous.

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 explicit when-to-use guidance ('deep audit before or alongside verification'), a recommended pipeline order (search_datasets → get_evidence → verify_evidence → verify_attestation), and clear exclusions ('do not use it for a live transport check or signature verification—use verify_evidence or verify_attestation instead'). This is model usage guidance.

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