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disclosure_inspect

Generates a structured summary of a student's AI disclosure, covering assignment identity, AI usage facts, tools, role taxonomy, assistance extent, prompt details, artifact hash, policy posture, and signature.

Instructions

Structured summary of a Student AI Disclosure: assignment identity, AI usage facts, tools used (with back-refs to agent / tutor cards), role taxonomy, assistance extent, prompt-mode + count, artifact hash, policy posture, signature + acknowledgment.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
document_jsonYes
Behavior2/5

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

No annotations exist, so the description carries full burden. It describes the output but does not state whether the tool has side effects, requires authentication, or is read-only. For an inspection tool, the read-only nature is implicit but not explicit. Additionally, no information about errors, rate limits, or performance traits is given.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that efficiently lists all major output components. It is concise and front-loaded with the core purpose. However, it could be better structured (e.g., bullet points) for clarity, and it omits input details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that there is no output schema and only one input parameter, the description does a reasonable job of explaining the output contents. However, it fails to document the input parameter or the tool's behavior beyond the output. It covers the 'what' but not the 'how' or 'when', leaving gaps for an agent to use the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage, and the tool's description does not explain the single parameter 'document_json'. There is no indication of what format the JSON should be, what keys are expected, or how to construct the input. The description only mentions the output, leaving the parameter entirely opaque.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly specifies the tool's purpose: returning a structured summary of a Student AI Disclosure. It lists the specific elements included (assignment identity, AI usage facts, etc.), making the resource and output unambiguous. The name 'disclosure_inspect' and the verb 'Structured summary' align well, and among sibling tools like 'disclosure_validate' and 'disclosure_verify_artifact_hash', the inspect function is distinct enough.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No usage guidelines are provided. The description does not indicate when to use this tool over siblings like 'disclosure_validate' or 'disclosure_verify_artifact_hash'. There is no mention of prerequisites, input format expectations, or context for when this tool is appropriate.

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

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