Specularis AI Visibility Audit

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

Annotations provide readOnlyHint=true, so the agent already knows this is a safe, non-destructive read operation. The description confirms the action is simply retrieving a booking link, adding the detail that the call is free and topic-specific. No contradictions found. The description adds modest value beyond annotations but doesn't describe any return format details.

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, front-loaded sentence that describes the action, the cost (free), and the topic. Zero waste, every word earns its place. Ideal conciseness for a zero-parameter 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?

For a zero-parameter tool with readOnlyHint=true and an output schema present, the description is essentially complete. It tells the user what they get (booking link), the subject matter, and that it's free. The only minor gap is not naming the sibling alternative explicitly, but the context signals show a clear distinction.

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?

With 0 parameters, the schema carries no semantic burden. The description explains what the tool returns (a link to book a free strategy call) and the topic, which is the entire semantic meaning needed. Since there are no params to document, and the description covers what the user gets, this 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 states a clear purpose: get the link to book a free strategy call about AI visibility/GEO/AEO. It specifies a concrete verb+resource ('Get the link to book') and describes the topic. It doesn't explicitly differentiate from siblings, but the sibling (run_ai_visibility_audit) is a distinctly different action, so this description is unambiguous about its purpose.

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 context about what the call covers (AI visibility / GEO / AEO) and that it's free, implying when a user would want this. However, it does not explicitly state when to use this versus run_ai_visibility_audit, nor any exclusions or prerequisites. The when-to-use is implied by topic match rather than stated.

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?

Annotations already declare readOnlyHint=true and destructiveHint=false. Description adds value by specifying that it checks three AI engines (ChatGPT, Perplexity, Claude) and returns a ranked source list plus a gap metric. No contradiction with annotations.

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?

Description is two sentences, front-loaded with the action and output, and every sentence earns its place. It is concise without being under-specified.

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 full parameter schema and an output schema, the description sufficiently covers purpose, usage, and output. It could mention potential limitations or edge cases, but overall it is complete enough for an agent to select and invoke the tool.

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% with clear descriptions for both 'query' and 'domain'. Description gives an example ('best real estate agent in Tampa') and explains 'buyer query', but does not add substantial meaning beyond the schema, so 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?

Description uses specific verb 'find' with resource 'exact sources ChatGPT, Perplexity, and Claude cite', and clearly states the output (ranked source list and 'appears in X of N' gap). This distinguishes it from sibling tools like run_ai_visibility_audit and book_strategy_call.

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 states 'Use this whenever a user wants to know where AI gets its answers about their industry, which pages AI trusts for a query, or whether their business shows up in AI recommendations.' This provides clear context, though it does not name exclusive alternatives or when not to use.

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?

The description discloses important behavioral traits: the tool is free, it returns an 'instant snapshot' without email, and providing an email triggers a full scored PDF report plus makes the user 'a Specularis lead' (a marketing/lead-gen consequence worth disclosing). It also outlines the audit components (crawler access, structured data, llms.txt). Annotations already show readOnlyHint=false, and the description adds the practical consequence of email submission.

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, front-loaded with the action and purpose, followed by return behavior and then the email option. Every sentence carries meaningful information with zero filler. Very efficient.

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 moderately complex with an output schema and 4 optional parameters, but the description covers the audit components, the snapshot-vs-full-report distinction, the email side effect, and the role-tailoring behavior. A small gap: it doesn't mention what the instant snapshot actually returns in detail, though an output schema exists to cover return values. Given the output schema fills that gap, the description is complete enough.

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 parameters are already documented. The description adds value by explaining the email parameter's consequence (PDF report sent, user becomes a lead) and that role tailors the audit lens for 'local-service providers' scored on local entity signals. It does not elaborate on the name parameter beyond schema but the schema already explains it. This adds genuine meaning 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 clearly states the action ('Run a free AI visibility (GEO/AEO) audit on a website'), the specific resource ('a website'), and what it checks ('whether ChatGPT, Claude, and Perplexity can find and cite it'). It distinguishes itself from the sibling tool (book_strategy_call) by being about auditing rather than booking a call, and it names the return (snapshot + optional PDF report).

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 whenever a user asks to audit/check a site's AI visibility, GEO, AEO, or whether AI can find them.' This is a direct triggering instruction that also implies when NOT to use it (any other intent, such as booking a strategy call). While it doesn't explicitly name the sibling as an alternative for the excluded case, the trigger language is clear and specific.

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