GoAI Moat Claim Verifier

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

With no annotations provided, the description must carry the behavioral burden. It says 'audit' but does not disclose whether it is read-only, what output it produces (though an output schema exists), any auth or rate-limit requirements, or any constraints like text length limits. The description adds minimal behavioral context beyond 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 description is a single, clear sentence that front-loads the core action and specific scope. Every word earns its place, with no filler or redundant information.

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 simple one-parameter tool with an output schema present, the description is mostly sufficient. It clearly states the tool's purpose and the type of content it handles. Minor gaps include lack of explicit guidance on selection versus siblings and no mention of limitations (e.g., language or length), but these are not critical for basic use.

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% for the single 'text' parameter, so the schema already documents its type and a basic description. The tool description adds meaning by specifying that the audit focuses on unsourced data assertions, which clarifies the purpose of the text parameter. However, it does not add format, length, or example details 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 states a specific verb ('audit'), resource ('text'), and scope ('unsourced data assertions - numbers, percentages, rankings'). This clearly distinguishes it from sibling tools like verify_claim (which likely checks a specific claim) and zero_fiction_checklist (a fiction-specific checklist).

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?

There is no explicit guidance on when to use this tool versus its siblings. The description only states what it does, leaving it to the agent to infer that it's for auditing text for unsourced data assertions. No exclusions or alternative recommendations are provided.

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?

With no annotations provided, the description carries the full burden of behavioral disclosure. 'Verify' implies a read-only lookup against a library, but the description does not state whether the tool writes anything, requires authentication, has rate limits, or how it handles claims not found in the library. For a tool with zero annotation coverage, this is a meaningful gap.

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?

A single sentence with zero wasted words. The verb and resource are front-loaded, and the qualifying context ('zero-fiction fact library') is placed at the end. Every word earns its place.

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?

An output schema exists, so return values are covered. With only one well-documented parameter, the tool is structurally simple. However, the phrase 'zero-fiction fact library' is unexplained — an agent may not know what this library contains or how verification results are expressed. Slightly more context on the verification process would improve completeness.

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 the schema fully documents the single 'claim' parameter, including a concrete example. The description adds no additional meaning about the parameter beyond what the schema provides. Baseline 3 is appropriate when the schema does the heavy lifting.

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 states a specific verb ('verify') and resource ('a business claim') with an identifiable context ('zero-fiction fact library'). It is clear about what the tool does. However, it does not explicitly distinguish itself from its siblings (audit_text, zero_fiction_checklist) — the distinction is only implied by the different verbs and noun phrases in their names.

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 given on when to use this tool versus its siblings. There is no mention of when verification is appropriate, what kinds of claims it handles, or when audit_text or zero_fiction_checklist would be the better choice. The agent is left to infer usage from the name alone.

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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states that the tool returns a checklist, which implies a read-only operation, but it does not explicitly say it is non-destructive or that it has no side effects. For a simple retrieval tool, this is adequate but could be more explicit.

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, concise sentence that front-loads the verb 'Return' and immediately states the resource and purpose. There is no wasted wording, and it is appropriately sized for a simple tool.

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 that the tool has no parameters and an output schema exists, the description adequately covers the purpose and usage context. It does not explain the checklist contents, but that is likely in the output schema. No critical information is missing for an agent to invoke it correctly.

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 tool has zero parameters, so the baseline is 4. The description does not need to add parameter details since there are none. It correctly omits any parameter discussion.

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 verb 'Return' and the specific resource 'zero-fiction self-check checklist', along with the context 'for AI agents before outputting business advice'. This distinguishes it from sibling tools like audit_text and verify_claim, which focus on auditing or verification rather than returning a checklist.

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 a clear usage context: it is meant to be used before outputting business advice. However, it does not explicitly mention when not to use it or how it differs from audit_text and verify_claim. The guidance is implied rather than explicit.

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