GoAI Moat AI Visibility Audit

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

With no annotations, the description carries the full burden and does well: it discloses tier gating, per-email limits, programmatic entitlement via keys, and the degraded output when unauthenticated. It stops short of describing runtime characteristics such as rate limits, latency, or external API calls, which would complete the picture for a long-running audit.

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?

Front-loaded with the core purpose, followed by tightly organized tier sections that each earn their place. Formatting is slightly shouty (ALL-CAPS tier labels) but no sentence is 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?

An output schema exists, so return values need not be explained, and the description covers auth paths, credentials, and tier behavior thoroughly. The only gap is that it never mentions the optional category or score parameters in prose, but the schema descriptions are complete for those.

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, but the description adds real meaning: it clarifies the relationship between api_key and license_key, that email unlocks exactly one free audit, and that omitting credentials changes the result to framework-only. That is more than the schema's per-field strings convey.

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?

States a specific verb+resource ('deep-audit a brand's visibility') and names the surfaces measured (ChatGPT, Perplexity, AI Overviews). This is clearly distinguishable from the sibling tools (checklist, category leaders, fix priority, usage, license check), so an agent can route to it without opening the schema.

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?

Explicitly lays out the tier conditions: email unlocks one free full audit, api_key/license_key enables unlimited programmatic runs, and without either you get only the framework and tier mapping. It gives clear when-to-use context but does not name alternatives or state 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.

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

With no annotations, the description carries the full burden, and it does disclose two meaningful traits: the key is validated server-side and the call produces an api_key as a side effect of activation. It still omits whether the call is idempotent, what happens on an invalid or already-used license, and whether it consumes a license seat.

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 front-loads the action and the payoff with zero filler; the parenthetical about server-side validation earns its place as the only behavioral detail offered.

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 two-parameter tool with an output schema, the description covers the essentials and even names the returned artifact (api_key). Failure modes and repeat-call behavior are the remaining gaps, but nothing critical to invoking it 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?

Schema description coverage is 100%, so both parameters are already documented, including the email-binding behavior. The description's mention of the license key adds no syntax or format detail beyond the schema, so this sits at the baseline.

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 (activate), the resource (a Gumroad license), and the concrete outcome (a GoAI Moat api_key). The siblings listed are unrelated analytics tools, so there is no real ambiguity to resolve; the only friction is that the tool name 'check_license' understates that this is an activation, not a read-only check.

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 when-to-use or when-not-to-use guidance, no prerequisites (e.g. must own a Gumroad purchase), and no alternatives named. The only implied context is that the input is a purchased license key, which is already conveyed by the schema.

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.