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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 queries with different winners. Shapes the same. 'Best sales engagement platform for SMB teams' is not the same query 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 queries 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 query. 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 query. 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_questionsNoQueries your buyers ask. Queries 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 queries 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.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changed
    • changedInput schema / properties / buyer_questions / description
      Previous value: -"Questions 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."New value: +"Queries your buyers ask. Queries 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 queries 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."
    • changedInput schema / properties / geo_scope / description
      Previous value: -"Where 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."New value: +"Where do you sell? Getting this wrong skews the whole report: a national firm scored on one city's queries 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 query. 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."
    • changedInput schema / properties / locations / description
      Previous value: -"Which 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."New value: +"Which places? The country you sell across, or the cities you are bound to. Shapes which geography goes into a localized buying query. Omit it rather than guessing: an omitted answer is recorded as unknown, a guessed one is indistinguishable from a real answer."
    • changedInput schema / properties / sells_to / description
      Previous value: -"What 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."New value: +"What size and type of customer? SMB and enterprise are different queries with different winners. Shapes the same. 'Best sales engagement platform for SMB teams' is not the same query 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."
  2. Changed8 schema fields changed
    • addedInput schema / properties / buyer_questions / description
      Added value: +"Questions 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."
    • addedInput schema / properties / capacity / description
      Added value: +"Who 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."
    • addedInput schema / properties / competitors / description
      Added value: +"Who 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."
    • addedInput schema / properties / domain / description
      Added value: +"The company's website or domain, e.g. \"acme.com\"."
    • addedInput schema / properties / email / description
      Added value: +"The user's work email. Required: we send the finished report there and it identifies the account. One free scan per email per month."
    • addedInput schema / properties / geo_scope / description
      Added value: +"Where 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."
    • addedInput schema / properties / locations / description
      Added value: +"Which 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."
    • addedInput schema / properties / sells_to / description
      Added value: +"What 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."
  3. Changed6 schema fields changed
    • addedInput schema / properties / buyer_questions
      Added value: +{
      +  "anyOf": [
      +    {
      +      "items": {
      +        "type": "string"
      +      },
      +      "type": "array"
      +    },
      +    {
      +      "type": "null"
      +    }
      +  ],
      +  "default": null,
      +  "title": "Buyer Questions"
      +}
    • addedInput schema / properties / capacity
      Added value: +{
      +  "default": "",
      +  "title": "Capacity",
      +  "type": "string"
      +}
    • addedInput schema / properties / competitors
      Added value: +{
      +  "anyOf": [
      +    {
      +      "items": {
      +        "type": "string"
      +      },
      +      "type": "array"
      +    },
      +    {
      +      "type": "null"
      +    }
      +  ],
      +  "default": null,
      +  "title": "Competitors"
      +}
    • addedInput schema / properties / geo_scope
      Added value: +{
      +  "default": "",
      +  "title": "Geo Scope",
      +  "type": "string"
      +}
    • addedInput schema / properties / locations
      Added value: +{
      +  "anyOf": [
      +    {
      +      "items": {
      +        "type": "string"
      +      },
      +      "type": "array"
      +    },
      +    {
      +      "type": "null"
      +    }
      +  ],
      +  "default": null,
      +  "title": "Locations"
      +}
    • addedInput schema / properties / sells_to
      Added value: +{
      +  "anyOf": [
      +    {
      +      "items": {
      +        "type": "string"
      +      },
      +      "type": "array"
      +    },
      +    {
      +      "type": "null"
      +    }
      +  ],
      +  "default": null,
      +  "title": "Sells To"
      +}
  4. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=false and destructiveHint=false, so the description is not needed to state that. The description adds valuable behavioral details: the scan runs in the background (1–2 minutes), returns a report_id and report_url, may reuse an existing report when the monthly free scan is used, and reports missing intake answers. This goes well beyond the annotations and gives the agent a clear model of side effects and workflow.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

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

The description is substantial but justified for an 8-parameter async tool. It is front-loaded with purpose, then explains workflow, usage guidance, and returns in a logical order. While some sentences are lengthy, they all carry meaningful content and no repetition. It earns a 4 rather than a 5 due to minor redundancy (e.g., restating the free-scan quota that also appears in the schema), but it remains efficient.

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?

For a complex tool with no output schema, the description fully covers what an agent needs: what it does, how long it takes, what to ask the user, how to handle unknown values, what it returns (report_id, report_url, reuse status, missing answers), and the next step (call get_visibility_report). Combined with thorough schema descriptions for each parameter, nothing essential is missing.

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

Parameters4/5

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 tool description adds extra value by singling out geo_scope and sells_to as the parameters to always ask about, explaining their impact ('a firm that sells across one country, scored on one city's questions, looks invisible') and reiterating the omit-vs-guess policy. It also confirms that parameter descriptions match the intake contract, which helps the agent trust them. This lifts the score above baseline.

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 opening sentence states a specific verb ('Start'), a clear resource ('a free AI-visibility scan'), and the target ('a B2B company's website'). It further clarifies what the scan checks (how often AI engines name the company, finding gaps) and explicitly names the sibling tool (get_visibility_report) that reads the results, making the distinction obvious.

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?

The description gives direct, actionable guidance: 'ASK THE USER for geo_scope and sells_to before calling, if you do not already know them.' It explains why guessing is worse ('Guessing is worse than asking') and how to handle unknown values ('omit the field rather than inventing a plausible value'). It also tells the agent to call get_visibility_report afterward, effectively routing the workflow.

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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