Fetch a website and return the content as Markdown. Best practices: 1) Always set startCursor=0 for initial requests, and use the fetchedBytes value from previous response for subsequent requests to ensure content continuity. 2) Set contentSizeLimit between 20000-50000 for large pages. 3) When ha...
AI agents call fetch_markdown to retrieve information from Mult Fetch without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool retrieves and transforms existing web content into Markdown format. It has no capability to modify, delete, execute code, or trigger side effects on target systems. The chunking and cursor management features are mechanisms for efficient retrieval of large content, not write or destructive operations.
From the tool's definition Tool description states: 'Fetch a website and return the content as Markdown'. The verb 'fetch' and the stated purpose of retrieving and converting web content indicate data retrieval only.
Documented attack patterns abuse exactly the kind of access fetch_markdown gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Mult Fetch, and nothing reaches the server without passing your rules. This is the rule we recommend for fetch_markdown:
{
"version": "1",
"default": "deny",
"tools": {
"fetch_markdown": {}
}
}fetch_markdown is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Fetch a website and return the content as Markdown. Best practices: 1) Always set startCursor=0 for initial requests, and use the fetchedBytes value from previous response for subsequent requests to ensure content continuity. 2) Set contentSizeLimit between 20000-50000 for large pages. 3) When handling large content, use the chunking system by following the startCursor instructions in the system notes rather than increasing contentSizeLimit. 4) If content retrieval fails, you can retry using the same chunkId and startCursor, or adjust startCursor as needed but you must handle any resulting data duplication or gaps yourself. 5) Always explain to users when content is chunked and ask if they want to continue retrieving subsequent parts. It is categorised as a Read tool in the Mult Fetch MCP Server, which means it retrieves data without modifying state.
Register the Mult Fetch MCP server in PolicyLayer and add a rule for fetch_markdown: 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 Mult Fetch. Nothing to install.
fetch_markdown 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 fetch_markdown 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 fetch_markdown. 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.
fetch_markdown is provided by the Mult Fetch MCP server (lmcc-dev/mult-fetch-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Mult Fetch, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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5 Mult Fetch tools catalogued and risk-classified — across an index of 43,000+ MCP servers.