Audit Published Evidence Receipt

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

Annotations already mark it read-only, open-world, idempotent, and non-destructive. The description adds material behavioral context beyond those hints: it reads published pipeline evidence rather than performing a live fetch, and it explicitly tells the agent not to infer current reachability or semantic truth of the source. This is valuable epistemic boundary-setting that annotations do not express.

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 front-loaded with the core purpose, followed by the return contents, the critical live-fetch caveat, and the workflow placement. Every sentence earns its place; there is no redundant filler or restatement of the tool name.

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 single-parameter read-only tool with a robust annotation set and an output schema, the description is complete: it explains what is returned, what the tool is for, what it is not for, and where it fits in the broader tool sequence. Nothing an agent needs to decide whether to call this tool and interpret its result correctly 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?

There is only one parameter and the input schema already documents dataset_id with an example, a description, and the note that the tool does not fetch the live source. The tool description adds the context that this is for the complete published evidence receipt for one dataset, but it does not substantially change parameter understanding beyond the schema. Baseline 3 is appropriate given 100% schema coverage.

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 a specific action ('deep evidence audit', 'inspect a provenance and evidence receipt') and a specific resource ('complete published evidence receipt'), and enumerates the exact fields returned. It also distinguishes itself from live source fetches and positions itself relative to the verify workflow, so an agent can tell it apart from siblings like verify_evidence.

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 clear context: use it for a deep audit before or alongside verification, and the workflow chain 'search_datasets → get_evidence → verify_evidence → verify_attestation' provides ordering guidance. It also warns against using it to infer live source availability. It stops short of explicitly naming alternative tools to avoid, but the guidance is strong.

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