agentbuilt

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

With no annotations, the description carries the full behavioral burden and handles it well: it discloses cost, that it fetches a URL, the exact checks performed, the return shape (score, individual checks, fixes), and the negative boundary (no model recommendation queries). It stops short of a 5 by omitting details like rate limits or failure behavior for invalid URLs.

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?

Every sentence earns its place: the first states cost and core function, the second lists the specific checks, the third describes output and the key limitation. The description is front-loaded and contains no filler.

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 one-parameter tool with no output schema, the description covers input purpose, output content, and limitations well. The only gap is that it calls the paid alternative 'the paid audit' instead of naming request_visibility_audit directly, making sibling routing slightly less direct.

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 baseline is 3. The description reinforces that the URL is the site to fetch and audit but adds no format or normalization details beyond what the schema already provides with its example.

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 is explicit: 'Fetches a URL and reports how readable it is to AI assistants and agents' plus a concrete list of checks (robots.txt permissions, text without JavaScript, JSON-LD types, llms.txt, sitemap.xml, A2A card). It also distinguishes itself from the paid audit by stating what it does NOT do, so an agent can tell it apart from request_visibility_audit.

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 gives clear usage context: free, immediate URL-level legibility audit, and explicitly says it does not measure actual model recommendations, which is the paid audit. However, it references 'the paid audit' rather than naming the sibling tool to use instead, so the alternative routing is implied rather than fully 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?

With no annotations provided, the description carries the full behavioral disclosure burden. It discloses that the tool is 'Free', that the catalog contains pay-per-result tools, that tools are callable through various integrations, and that the caller uses their own Apify account. This is solid context for a read-only catalog listing, though it does not address response format or potential delays.

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 three sentences with no filler. It front-loads the key fact that it is free, immediately states what is returned, then adds usage context and integration details. Every sentence earns its place and the structure is easy to scan.

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 tool with one optional parameter and no output schema, the description is largely complete: it explains what is returned, what the catalog includes, when to use it, and cost/integration context. It does not describe the output format or the filtering behavior, but given the low complexity and the schema covering the only parameter, this is a minor 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% for the single optional 'task' parameter, and the schema already explains it as an optional filter. The description does not add parameter-level meaning beyond the schema, so the baseline score of 3 is appropriate. It does contextualize the catalog content, but that is not parameter semantics.

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 ('Returns') and resource ('the agentbuilt catalog of pay-per-result data tools'), and specifies what the catalog contains: what each tool does, when to use it, limitations, and cost. This clearly distinguishes it from the sibling audit tools, as it is a catalog/list operation rather than an audit action.

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 explicitly says 'Use this when choosing a tool' and gives concrete example use cases (YouTube transcripts, Substack archives, Shopify/ACP product feeds, AI-assistant visibility). It provides clear context but does not explicitly state when not to use it or mention the sibling tools as alternatives, so it misses the exclusion guidance that would make it a 5.

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 disclosure burden and does well: it reveals the external side effect (emails a report), the scope of external interaction (queries four named AI platforms), and the safety boundary around email ownership. It doesn't mention potential latency or that this creates external third-party interactions beyond email, but the core behavioral traits are disclosed.

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?

Three sentences, each earning its place: the first explains the mechanism and deliverable, the second and third give the required consent and anti-speculation guardrails. The most important information is front-loaded, and there is zero filler.

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 description substitutes well for a missing output schema by telling the agent what the report contains (mention rate, citation rate, competitors), and it fully covers the safety-critical consent condition. The only gap is not positioning this against audit_ai_readiness, but everything needed to call the tool correctly is present.

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 all five parameters meaningfully, which sets the baseline at 3. The description reinforces the consent/email relationship ('do not submit an address that is not theirs') and the purpose of notes (competitors to compare), adding marginal value, but it doesn't carry the parameter documentation burden.

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: 'Requests the free human-readable audit' and details exactly what the tool does (asks buyer questions to ChatGPT, Perplexity, Gemini and Claude, emails a report on mention rate, citation rate, and competitor mentions). This clearly distinguishes it from siblings like list_agentbuilt_tools and audit_ai_readiness by describing the concrete deliverable.

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 gives explicit when-to-call conditions: 'Only call this when the person you are acting for has asked for it and has given you their own email address' and explicit when-not-to-call rules ('Do not call it speculatively, and do not submit an address that is not theirs'). It lacks explicit routing to the sibling audit_ai_readiness as an alternative, so it falls just short of a 5.

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