Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass _apiKey to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, con...
AI agents call ai_visibility_check to retrieve information from Mcp Nager Holidays without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
| Parameter | Type | Required | Description |
|---|---|---|---|
entity | string | Yes | The thing to ask about. Brand/business name, product name, person, or topic. E.g. "Pipeworx", "OpenInvoice", "Acme Corp pricing". |
models | array | — | Which models to probe. Supported: "workers-ai" (free default), "anthropic" (requires _apiKey). Omit for just workers-ai. |
_apiKey | string | — | Optional Anthropic API key (sk-ant-...) — only needed if "anthropic" is in models. Passed straight through to api.anthropic.com. |
context | string | — | Optional: a phrase locating the entity (e.g. "Boston restaurant", "B2B SaaS"). Helps disambiguate common names. |
Parameters from the server's own tool schema.
This is a read-only information retrieval tool. It probes LLMs for knowledge about topics and returns scoring data. There are no side effects, no data modification, no code execution, and no destructive operations. The worst case misuse is retrieving or inferring competitive intelligence, which is a low-severity Read risk.
From the tool's definition Tool description states it 'probe[s] one or more LLMs for what they know' and 'Returns per-model {score, confidence, signals, raw_response}'.
Attacks that exploit this kind of access
Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model. Default model is Workers AI Llama-3.3-70b (free); pass _apiKey to also probe Anthropic (BYO key — you pay Anthropic directly for those calls). Returns per-model {score, confidence, signals, raw_response} + a combined view. Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring. It is categorised as a Read tool in the Mcp Nager Holidays MCP Server, which means it retrieves data without modifying state.
ai_visibility_check accepts 4 parameters: entity, models, _apiKey, context. Required: entity. The full parameter table on this page comes from the server's own tool schema.
Register the Mcp Nager Holidays MCP server in PolicyLayer and add a rule for ai_visibility_check: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Mcp Nager Holidays. Nothing to install.
ai_visibility_check is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the ai_visibility_check rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for ai_visibility_check. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
ai_visibility_check is provided by the Mcp Nager Holidays MCP server (pipeworx-io/mcp-nager-holidays). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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