ai_evidence_inspect

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

With zero annotations, the description carries the full burden. It describes the output content but does not disclose behavioral traits: whether the tool is read-only, what happens on invalid input, error handling, or side effects. The agent must infer that it is a read operation without confirmation.

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 sentence, concise and free of fluff. However, it could be better structured with an action verb at the start and clearer separation between the output description and the listed fields.

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 single parameter and absence of output schema, the description should more fully explain input expectations and output format. It mentions fields but fails to specify the return type (e.g., JSON object) or what the structured summary looks like. For a simple tool, this is incomplete.

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?

The parameter 'document_json' has no description in the schema (0% coverage). The tool description does not clarify that this parameter is the input JSON representing the AI Evidence object, nor does it specify the expected format or constraints. The agent is left to guess the parameter's role.

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 it returns a structured summary of an AI Evidence object and lists key fields (claim text, source metadata, etc.), which helps distinguish it from sibling tools like ai_evidence_validate or ai_evidence_verify_hash. However, it uses a noun phrase ('Structured summary...') instead of an active verb ('Returns...'), which slightly reduces clarity for an AI agent.

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?

No guidance is provided on when to use this tool versus alternatives (e.g., ai_evidence_validate for validation or ai_evidence_verify_hash for hash verification). There is no mention of prerequisites, typical use cases, or when not to use it.

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