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 invoke ai_visibility_check to trigger actions in Expression Atlas. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.
| 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 tool triggers external API calls to LLM services (Workers AI, Anthropic) to probe brand/topic visibility. It executes queries against external services and may incur costs (especially with BYO Anthropic API key). It is not merely reading local data — it actively invokes external model inference endpoints.
From the tool's definition Probe one or more LLMs for what they know about a business / brand / product / topic... pass `_apiKey` to also probe Anthropic... triggers external operations (LLM API calls) whose effects depend on arguments
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 Execute tool in the Expression Atlas MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
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 Expression Atlas 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 Expression Atlas. Nothing to install.
ai_visibility_check is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
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 Expression Atlas MCP server (https://gateway.pipeworx.io/expression-atlas/mcp). 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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