attest — verification for AI agent artifacts

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

Beyond the annotations (readOnly, idempotent, closed-world), it discloses that verification is rule-based with no LLM and fully deterministic ("the same artifact always yields the same result"), which tells the agent the result is reproducible and cacheable. It also names the non-obvious provenance/mandate checks that the caller cannot infer from the name.

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 purpose and the payoff (before accepting/paying) are front-loaded in the first sentence, and the four layers are compactly enumerated. Every sentence carries information the agent needs; nothing is padding.

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?

There is no output schema, but the description compensates by naming the return shape: verdict (accept/review/reject), 0-100 trust score, findings, and an Ed25519 signature. Combined with the safety annotations, this is sufficient for an agent to call the tool and interpret its result.

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 baseline is 3. The prose adds no per-parameter meaning (it never explains how `schema` is applied or that `source` is only recorded in the attestation) beyond stating the four verification layers, so the schema remains the sole source for parameter semantics.

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 gives a precise verb+resource (verify an artifact produced by an AI agent) and enumerates the four checks performed (schema conformance, emptiness, provenance bound to hash, signed mandate), so the agent knows exactly what the tool inspects. It does not differentiate from the sibling bridge_check, which is the only missing element for a 5.

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?

It states clear usage context: "Use before accepting work from another agent and before payment." That is an explicit trigger condition rather than an implied one. It offers no when-not guidance and does not mention the alternative sibling bridge_check, so it stops short of a 5.

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

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

Annotations already declare read-only, idempotent, non-destructive, closed-world behavior. The description adds useful behavioral context by describing the per-artifact verdict and batch summary counts, though it does not explain verdict criteria or batch limits.

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, front-loaded with the batch action and result format, then a concrete usage condition. There is no wasted text.

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 annotations covering safety and schema covering all parameters, the description supplies the missing return behavior at a high level. It remains slightly incomplete on verdict structure, batch size limits, and how schema failures are reported.

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 schema already documents artifacts, optional schema, and source. The description gives examples of artifact types but adds no parameter-specific syntax or constraints beyond the schema.

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 verb and resource ('verify several artifacts at once') and clarifies batch scope versus the singular sibling verify_artifact. It also specifies the output shape: one verdict per artifact plus an acceptance/review/rejection summary.

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?

It gives a clear triggering condition: use when an agent has produced a batch of artifacts that each need checking before payment. It does not explicitly say when not to use it or directly name verify_artifact for single-item cases, so it falls short of full alternative routing.

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