GoAI Moat AI Visibility Audit

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

No annotations are provided, so the description carries the behavioral disclosure burden. It does disclose the free-tier cap, the developer/unlimited path, and the degraded output when no credentials are supplied. It stops short of mentioning side effects or external calls, but there is no contradiction with the schema.

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 front-loaded with the core purpose and then uses compact, scannable tier headings. Every sentence adds actionable information—no filler or repetition. The credential source URL is included exactly where it is needed.

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-value details do not need to be in the description. The description sufficiently covers the required brand_name, optional auth paths, and the outcome when credentials are missing. It does not address error handling or invalid-key behavior, but the sibling check_license tool and output schema reduce that gap.

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%, providing the baseline of 3. The description adds meaning beyond the schema by mapping email, api_key, and license_key to their tier consequences and noting that score unlocks a free tier plus fix plan. This helps an agent choose the right parameter combination.

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 and resource: 'Deep-audit a brand's visibility in AI answers' with concrete examples (ChatGPT, Perplexity, AI Overviews). It is clearly distinct from the sibling helper tools like get_checklist or get_usage because it names the primary audit action rather than a supporting utility.

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 explicit, decision-relevant usage guidance: email unlocks one free audit, api_key/license_key enables programmatic use, and absence of both returns only the framework/tier mapping. It does not explicitly compare against sibling tools, but the authentication-dependent instructions are clear and practical.

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 communicates that this is a validation/check operation rather than a mutation, which is useful, but it does not state whether the check calls the Gumroad API, whether network access is needed, or what happens with invalid/expired keys. The output schema likely covers return details, so the remaining gap is moderate.

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, front-loaded sentence that states the exact purpose without filler. Every word contributes to understanding what the tool validates and under what condition.

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 single-parameter validation tool with an output schema, the description is largely sufficient: an agent can infer the invocation intent and the expected domain. It loses one point because it does not explicitly orient the agent toward when this check should be performed relative to the sibling audit tools.

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 schema describes 'license_key' as 'The license key received after purchase,' and the description adds that it is a Gumroad key and ties it to unlimited-audit validity. Since schema coverage is 100%, the description need not add much, but it provides only minor extra context about 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 uses a specific verb ('Check') with a specific resource ('Gumroad license key') and a concrete validity condition ('valid for unlimited audits'). This clearly distinguishes it from the sibling tools, which address auditing, checklists, and fix prioritization rather than licensing.

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 wording implies the tool is for verifying a license before using audit-related capabilities, but it does not explicitly state when to use it versus alternatives or mention any prerequisites such as requiring a prior purchase. Its role is inferable from the domain, but the guidance is not explicit.

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?

No annotations are provided, so the description carries the full disclosure burden. It discloses the free tier, the constrained category list, and the underlying methodology (combining public market data with a geo-footprint audit). It omits any mention of auth requirements, rate limits, or how the two data sources are weighted, though the output schema mitigates some return-format ambiguity.

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?

Roughly 70 words for a read-only query tool with one parameter is appropriately sized. The primary purpose is front-loaded in the first sentence, followed by the data-source explanation and usage examples. No filler or repetition, though the trailing 'Free tier' sentence could arguably be merged with the category constraint for tighter structure.

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 simplicity (1 param, 100% schema coverage, output schema present), the description covers the core bases: what it returns, what data feeds it, when to call it, and its constraints (free tier, two available categories). Nothing critical 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?

Schema coverage is 100% — the schema already documents the category parameter as a slug with examples. The description's category list (connector, phone_case) reinforces the schema but adds no new semantic depth beyond what's already in the parameter description. Baseline 3 applies since 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 opens with a specific verb and resource ('Return the AI-recommendation leaderboard for a product category'), and explains the data combination (listed-market + tracked B2B brands). This clearly distinguishes it from siblings like audit_ai_visibility (a visibility audit) and get_checklist (a checklist), which serve different purposes.

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 concrete query examples ('who are the top Chinese suppliers of X', 'which brands does AI recommend in X') that an agent can match against user intent. It also lists the two available categories as a hard constraint. It stops short of naming explicit alternatives or stating when NOT to use it, but the usage context is well-articulated.

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, the description carries the behavioral disclosure burden. It implies a read-only retrieval by saying 'Return', and 'Free' adds a useful cost/access signal, but it does not mention authentication, rate limits, or explicitly state that no action is performed. This is thin but acceptable for a trivial zero-parameter getter.

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?

One efficient sentence front-loads the core purpose, adds useful structural detail in parentheses, and includes a short cost signal ('Free'). No words are wasted.

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?

The output schema covers the return shape, and the description provides enough context for a zero-parameter call: what the checklist is, how many items it contains, and that it is free. Nothing needed to invoke correctly is missing.

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 and full schema coverage, so the description has no parameter-semantics burden. The description appropriately adds nothing about parameters, and the baseline of 4 applies.

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'), the specific resource (the full 30-point AI visibility checklist), and its structure (5 categories × 6 checks). It does not explicitly differentiate itself from the sibling audit_ai_visibility, leaving some distinction to inference.

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 when-to-use guidance or alternative routing is provided. The word 'Free' hints at access/cost but does not tell an agent when to prefer this tool over audit_ai_visibility, check_license, or get_fix_priority.

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?

No annotations are provided, so the description carries the full burden. It states the transformation (score to tier/plan) and that it's 'Free', but doesn't disclose what the output looks like, whether it's a read-only operation, or any side effects. The output schema exists but the description doesn't add behavioral context beyond the basic transformation.

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 that is efficient and front-loaded with the core action. The word 'Free' is a minor addition that may be useful context but is not essential. No wasted words.

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, the description is mostly adequate. However, it doesn't explain what the output schema contains (tier and plan details) or any prerequisites (e.g., must have completed the checklist). The output schema likely covers return values, but the description could be more complete about the transformation logic.

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 the single parameter 'score' as the total from the 30-point checklist. The description adds the range 0-30 and the concept of a checklist, which is slightly redundant with the schema but reinforces the meaning. Baseline 3 is appropriate.

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's function: converting a 0-30 visibility score into a tier and prioritized fix plan. It uses a specific verb ('Turn') and identifies the resource (visibility score). It doesn't explicitly differentiate from siblings, but the function is distinct enough from audit_ai_visibility, check_license, and get_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 implies usage: when you have a 0-30 visibility score and need a tier/plan. It doesn't explicitly state when not to use it or name alternatives. The sibling tools suggest related but different functions, but no explicit routing guidance is given.

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?

No annotations are provided, so the description carries the transparency burden. The verb 'Report' and listed metrics signal a non-mutating read operation, but the description does not explicitly mention auth requirements, rate limits, or side effects.

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?

One compact sentence that front-loads the verb and object and packs the key output fields into a parenthetical. No filler or repetition.

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?

The tool is simple: one optional parameter confirmed by the schemaaine and an output schema is present. The description is largely sufficient for correct invocation, though it would be more complete with explicit usage guidance or behavioral caveats.

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% and the single optional api_key parameter is already documented in the schema. The description does not add further parameter-level meaning, so baseline 3 applies.

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 ('Report') and resource ('the caller's API usage volume'), and enumerates concrete data included: total audits, last called, recent brands. This distinguishes it clearly from siblings like check_license or get_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 implies the use case (reporting API usage) but does not explicitly compare this tool to alternatives such as check_license or provide when/when-not conditions. It offers enough context for an agent to infer when it applies, but no exclusions.

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