ai_visibility_check
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...
This record as markdown: /tools/io-github-pipeworx-io-linear/ai-visibility-check.md
What ai_visibility_check does on Linear
AI agents call ai_visibility_check to retrieve information from Linear without modifying anything. It is 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.
Why ai_visibility_check is rated Low
This tool performs read-only queries against external LLMs to gather information about brand/product visibility. It retrieves and scores data without side effects—no resources are created, modified, deleted, or executed. While it optionally consumes Anthropic API credits (user-provided keys), the tool itself does not initiate financial transactions; the user explicitly opts in and controls payment.
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}'. Core function is querying/retrieving information about LLM training data visibility.
Attacks that exploit this kind of access
The rule that runs ai_visibility_check safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Linear, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For ai_visibility_check, this is the rule to start with:
ai_visibility_check is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Linear, apply this rule, and every ai_visibility_check call is checked against it from then on.
Questions about ai_visibility_check
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 Linear 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 Linear 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 Linear. 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 Linear MCP server (https://gateway.pipeworx.io/linear/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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