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

No annotations are provided, so the description carries full burden. It discloses key behavioral traits: grounding against 'CURRENT public gov facts', screening against federal exclusion lists, and running trap-fact refusals. It also clarifies the output is 'an EVIDENCE ARTIFACT with a hash-chain receipt — NOT a 'you are compliant' claim'. While it doesn't detail error handling or idempotency, it provides substantial transparency for an initial evaluation.

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, dense paragraph that front-loads the main action and then explains the three key steps. Every sentence adds value without repetition or fluff. It is concise yet comprehensive for the tool's core functionality.

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 tool's complexity (orchestrating grounding, screening, trap-facts) and the absence of an output schema, the description gives a good overview but lacks details on the exact structure of the EVIDENCE ARTIFACT, error scenarios, or prerequisites (e.g., need for API keys). It provides enough to understand what it does, but not enough to fully anticipate behavior.

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%, so baseline is 3. The description adds meaning beyond the schema: it explains that 'traps' are 'planted false facts refuted by live public data' and that 'entities' are screened against federal exclusion/sanctions lists (OFAC/LEIE/SAM). This enriches understanding of optional parameters, justifying a higher score.

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 the tool builds a dated, source-linked, SIGNED compliance-evidence pack for regulated-AI deployers. It specifies the verb 'build', the resource (evidence pack), and differentiates from sibling tools like ground_claim, screen_entity, and verify_pack by encompassing multiple steps (grounding, screening, trap-facts) and producing an artifact.

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 provides context ('for a regulated-AI deployer to file with their auditor') but does not explicitly state when to use this tool versus alternatives such as ground_claim, screen_entity, or verify_pack. It implies usage for a complete pack, but lacks clarity on when to choose this over simpler tools.

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