Fetch a JSON file from a URL. 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 files. 3) When handling large content, ...
AI agents call fetch_json 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 is fundamentally a data retrieval operation (Read category). Severity is medium rather than low because: (1) it can fetch from arbitrary URLs, potentially exposing the system to SSRF attacks if not properly validated; (2) it retrieves potentially sensitive JSON data without apparent access controls; (3) the chunking and retry mechanism could be exploited to exfiltrate large amounts of data.
From the tool's definition Tool name is 'fetch_json' and description states 'Fetch a JSON file from a URL' with no modification, deletion, or execution capabilities. It retrieves and extracts content from remote resources.
Documented attack patterns abuse exactly the kind of access fetch_json 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_json:
{
"version": "1",
"default": "deny",
"tools": {
"fetch_json": {}
}
}fetch_json 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 JSON file from a URL. 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 files. 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_json: 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_json 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_json 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_json. 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_json 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.