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 Usgs Earthquakes. 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 actively calls out to external LLM APIs (Workers AI, Anthropic) with user-supplied arguments, triggering external operations and potentially incurring API costs on the user's behalf. It is not a simple read/query of local data — it executes remote inference calls.
From the tool's definition Probe one or more LLMs... pass `_apiKey` to also probe Anthropic... triggers external operations 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 Usgs Earthquakes 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 Usgs Earthquakes 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 Usgs Earthquakes. 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 Usgs Earthquakes MCP server (pipeworx-io/mcp-usgs-earthquakes). 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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