generate_llms_txt
Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt....
This record as markdown: /tools/io-github-pipeworx-io-linear/generate-llms-txt.md
What generate_llms_txt does on Linear
AI agents invoke generate_llms_txt to trigger actions in Linear. 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 |
|---|---|---|---|
url | string | Yes | Full URL of the site to summarize, e.g. "https://example.com" or a specific landing page. |
max_links | number | — | Maximum number of link entries to include (default 25, max 50). |
Parameters from the server's own tool schema.
Why generate_llms_txt is rated High
This tool performs an external network request (fetching a URL) and processes the response, which constitutes triggering an external operation. It is not a simple read of local data — it makes outbound HTTP calls to arbitrary URLs on behalf of the agent. The output is a generated text artifact. No data is written to a system or destroyed, but the external fetch makes this Execute rather than Read.
From the tool's definition Fetches the page, extracts title/description/key links — triggers an external HTTP fetch operation against an arbitrary URL, whose effects depend on the argument provided
Risk signalsAccepts URL/endpoint input (url) · Admin/system-level operation
Attacks that exploit this kind of access
The rule that runs generate_llms_txt 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 generate_llms_txt, this is the rule to start with:
generate_llms_txt stays usable, but rate-capped: a runaway agent can't fire it dozens of times a minute. 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 generate_llms_txt call is checked against it from then on.
Questions about generate_llms_txt
Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt. Useful for: getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor. It is categorised as a Execute tool in the Linear MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
generate_llms_txt accepts 2 parameters: url, max_links. Required: url. 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 generate_llms_txt: 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.
generate_llms_txt 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 generate_llms_txt 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 generate_llms_txt. 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.
generate_llms_txt 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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