TofuBofu AI Visibility
Server Details
Free AI visibility scan for any B2B company: see how often AI engines recommend it, plus the fixes.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4.8/5 across 2 of 2 tools scored.
The two tools are clearly distinct: one starts a scan, the other retrieves results. There is no overlap in functionality.
Both tool names follow a consistent verb_noun pattern (scan_ai_visibility, get_visibility_report).
With only 2 tools, it is slightly on the low end, but for a simple scanning service this is reasonable and well-scoped.
The two tools cover the entire workflow: initiating a scan and retrieving the report. No obvious gaps for the stated purpose.
Available Tools
2 toolsget_visibility_reportARead-onlyInspect
Fetch the results of an AI-visibility scan started with scan_ai_visibility.
Args:
report_id: The id returned by scan_ai_visibility.
Returns:
While running: {status: "running", progress}. When done: the visibility
score, how often AI mentions the brand, share of voice, top competitors
winning the answers, and the highest-priority fixes, plus the report_url.
| Name | Required | Description | Default |
|---|---|---|---|
| report_id | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description details the behavior beyond annotations: it returns immediate progress while the scan is running, and once complete it returns comprehensive data (visibility score, brand mentions, share of voice, top competitors, fixes, report_url). This aligns with the readOnlyHint=true annotation and adds significant behavioral context.
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 concise and well-structured: first sentence states the purpose, then parameter explanation, then return values. No redundant information; every sentence adds value.
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 fully covers the tool's lifecycle (running vs completed states), return structure, and prerequisite (scan_ai_visibility). Given the tool's simplicity and absence of an output schema, the description is complete for an agent to invoke and interpret results.
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?
The input schema provides only a name and type for report_id, but the description explains 'The id returned by scan_ai_visibility.' This adds necessary semantics, making it clear where the ID comes from. With 0% schema coverage, the description compensates well for the single parameter.
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 'Fetch the results of an AI-visibility scan started with scan_ai_visibility.' It uses a specific verb 'fetch' and resource 'results', and distinguishes this from the sibling tool scan_ai_visibility which starts the scan.
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 links this tool to scan_ai_visibility, indicating it should be used after starting a scan. It also describes the two possible states (running vs done), providing clear context on when results are available. However, it does not explicitly state when not to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
scan_ai_visibilityAInspect
Start a free AI-visibility scan for a B2B company's website.
Checks how often AI engines (ChatGPT, Claude, Perplexity, Gemini) 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.
Args:
domain: The company's website or domain, e.g. "acme.com".
email: The user's work email. Required, we send the finished report here
and it identifies the account. One free scan per email per month.
Returns:
report_id, a report_url to view live, and whether an existing report was
reused (free scan already used this month).
| Name | Required | Description | Default |
|---|---|---|---|
| Yes | |||
| domain | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds significant behavioral details beyond annotations: scan runs in background (1-2 minutes), requires work email, sends report there, one free scan per email per month, returns report_id, report_url, and reuse status. 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?
The description is well-structured: a one-sentence purpose, followed by detailed behavior, then parameter explanations, and finally return values. No unnecessary words, every sentence adds value.
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 the tool has only 2 required parameters, no output schema, and no nested objects, the description covers all necessary aspects: purpose, behavior, parameters, return values, and limitations. It is complete for effective agent use.
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 0%, but the description adds meaning: domain examples ('acme.com'), email is a work email required for sending the report and identifying the account. This fully compensates for the lack of schema descriptions.
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 'Start a free AI-visibility scan for a B2B company's website' with a specific verb and resource. It explains what the scan checks (AI engines naming the company) and distinguishes from the sibling tool get_visibility_report by directing the user to call that later with the returned report_id.
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 provides context on when to use this tool: to start a scan and then call get_visibility_report. It mentions a limitation (one free scan per email per month) but does not explicitly state when not to use it or exclude alternatives.
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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