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TofuBofu AI Visibility

scan_ai_visibility

Start a free AI-visibility scan for a B2B company's website.

Checks how often AI engines (ChatGPT, Claude, Perplexity, Gemini, Google AI
Mode, Microsoft Copilot) name the company when buyers ask for vendor
recommendations, and finds the gaps. The scan runs in the background
(roughly 1-2 minutes); call get_visibility_report with the returned
report_id to read the score and findings.

ASK THE USER for geo_scope and sells_to before calling, if you do not
already know them. Everything past `email` is optional and the scan runs
without it, but geo_scope changes EVERY question we generate: a firm that
sells across one country, scored on one city's questions, looks invisible
when it is not. Guessing is worse than asking, and asking costs one line of
conversation. Where you do not know, omit the field rather than inventing a
plausible value: an omitted field is recorded as unknown, and the report
says its framing was assumed.

Every parameter carries its own description, generated from the one intake
contract the in-app scan form renders from, so what you are told here and
what a customer is asked are the same question.

Returns:
    report_id, a report_url to view live, whether an existing report was
    reused (free scan already used this month), and which intake answers
    were missing, so you can offer to re-run with them.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
emailYesThe user's work email. Required: we send the finished report there and it identifies the account. One free scan per email per month.
domainYesThe company's website or domain, e.g. "acme.com".
capacityNoWho will do the fixes? We size and sequence the plan to match. A solo founder does not get a list built for a five-person team. One of: solo (Just me); one_marketer (One marketer); small_team (A small team, 2 to 4); full_team (A full team, 5 or more). Shapes the fix plan, which is generated in the tail after the engines answer. Omit it rather than guessing: an omitted answer is recorded as unknown, a guessed one is indistinguishable from a real answer.
sells_toNoWhat size and type of customer? SMB and enterprise are different questions with different winners. Shapes the same. 'Best sales engagement platform for SMB teams' is not the same question as 'for enterprise revenue orgs'. Omit it rather than guessing: an omitted answer is recorded as unknown, a guessed one is indistinguishable from a real answer.
geo_scopeNoWhere do you sell? Getting this wrong skews the whole report: a national firm scored on one city's questions looks invisible, and a local one scored nationally looks unwinnable. One of: global (Anywhere); national (Across one country); national_local (National, with a local angle); local (My own city or region). Shapes every buying question. This is the single highest-leverage answer here. Omit it rather than guessing: an omitted answer is recorded as unknown, a guessed one is indistinguishable from a real answer.
locationsNoWhich places? The country you sell across, or the cities you are bound to. Shapes which geography goes into a localized buying question. Omit it rather than guessing: an omitted answer is recorded as unknown, a guessed one is indistinguishable from a real answer.
competitorsNoWho would a buyer otherwise pick? Companies the user says a buyer would pick instead of them. We track whoever the engines name either way, so this is the user's own view rather than the whole comparison set. Shapes the comparison set, the mismatch between who you name and who the engines do, and which of those two a given finding is about. Omit it rather than guessing: an omitted answer is recorded as unknown, a guessed one is indistinguishable from a real answer.
buyer_questionsNoQuestions your buyers ask. Questions this company's buyers actually ask. They are put to the engines verbatim and tracked scan over scan, which is what makes a trend line mean anything. Shapes the questions themselves, pinned ahead of the generated ones. Omit it rather than guessing: an omitted answer is recorded as unknown, a guessed one is indistinguishable from a real answer.

TDQS

A4.7/5.0
Behavior5/5

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

Describes the background execution, return values (report_id, report_url, reuse flag, missing answers), and the impact of omitting fields. Explicitly warns against guessing, which is consistent with the openWorldHint. No contradictions 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.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is excessively verbose and repetitive, repeating the phrase 'Omit it rather than guessing: an omitted answer is recorded as unknown, a guessed one is indistinguishable from a real answer' for multiple parameters. This could be condensed significantly without loss of clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no output schema, the description fully covers the return values and the follow-up step of calling get_visibility_report. It also explains the rationale behind optional parameters, making the tool's behavior and expected results clear.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Even though schema coverage is 100%, the description enriches each parameter with meaningful context, such as geo_scope's impact on report accuracy and competitor's role in shaping the comparison set. This goes beyond mere naming.

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?

The description clearly states the tool's function: starting an AI-visibility scan for a B2B website. It distinguishes from the sibling tool by implying that get_visibility_report retrieves the results, while this tool initiates the scan.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly instructs to call get_visibility_report with the returned report_id to read results. Also advises asking the user for geo_scope and sells_to before calling, and explains that optional fields can be omitted, providing clear when-to-use guidance.

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

A4.7/5.0
Disambiguation5/5

The two tools have clearly distinct roles: one initiates a scan, the other retrieves results. No functional overlap or ambiguity.

Naming Consistency5/5

Both names follow the same verb_noun pattern ('scan_visibility' and 'get_visibility_report'), consistent and predictable.

Tool Count5/5

With only two tools, the set is minimal and focused, perfectly matching the simple scan-and-retrieve workflow without excess.

Completeness5/5

The tool pair covers the complete lifecycle of an AI-visibility scan: initiating it and fetching the resulting report. No missing steps are apparent.

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