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Specularis AI Visibility Audit

by io.github.AdevAarons

Find AI Citation Sources

find_ai_citations
Read-only

Given a buyer query (e.g. 'best real estate agent in Tampa') and a website domain, find the exact sources ChatGPT, Perplexity, and Claude cite when answering that query — and whether the domain appears in any of them. Returns the ranked source list (with which engine cites each) and an 'appears in X of N' gap. 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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesThe question a customer would ask AI, e.g. 'best personal injury lawyer in Miami'.
domainYesThe website to check for, e.g. example.com

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
domainYes
sourcesYes
appears_inYesHow many of those sources the domain currently appears in.
booking_urlYes
total_sourcesYesHow many distinct sources AI cites for this query.

TDQS

A4.2/5.0
Behavior4/5

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.

Conciseness5/5

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.

Completeness4/5

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.

Parameters3/5

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.

Purpose5/5

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.

Usage Guidelines4/5

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.

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TDQS

A3.8/5.0
Disambiguation4/5

The two tools have clearly distinct purposes: one books a sales call, the other runs an audit. There is no functional overlap in what each accomplishes, and the descriptions spell out their distinct triggers clearly.

Naming Consistency3/5

Both tools follow a verb_noun pattern (book_strategy_call, run_ai_visibility_audit) which is consistent in style. However, the verbs are different in form—'book' and 'run'—and there's no clear prefix family, though the pattern is uniform enough to be readable.

Tool Count3/5

Two tools is on the thin side for a server claiming to be an 'AI visibility audit' offering. While both tools make sense for lead generation and audit purposes, the surface feels minimal for the apparent domain scope.

Completeness3/5

The pair covers a lead-capture workflow (audit + strategy call), which is internally consistent. However, there are notable gaps—no way to check audit status, retrieve previous results, compare audits, or access settings—making this complete only for the narrow funnel of 'audit then book call'.

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