Low Risk

datafood_bundle

Bundle 1-20 cross-niche queries in one call. Saves 50-92% vs. per-API. Free preview accepts up to 5; paid via Stripe session_id or x402 X-Payment header.

Part of the Datafood Mcp server.

datafood_bundle is read-only, but an agent in a loop can still rack up calls and cost. PolicyLayer caps every call before it runs. Live in minutes.

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AI agents call datafood_bundle to retrieve information from Datafood Mcp without modifying any data. This is common in research, monitoring, and reporting workflows where the agent needs context before taking action. Because read operations don't change state, they are generally safe to allow without restrictions -- but you may still want rate limits to control API costs.

Even though datafood_bundle only reads data, uncontrolled read access can leak sensitive information or rack up API costs. An agent caught in a retry loop could make thousands of calls per minute. A rate limit gives you a safety net without blocking legitimate use.

Read-only tools are safe to allow by default. No rate limit needed unless you want to control costs.

policy.json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "datafood_bundle": {}
  }
}

See the full Datafood Mcp policy for all 4 tools.

Get this rule live on your own Datafood Mcp server in minutes. PolicyLayer enforces it on every call, before it runs.

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These attack patterns abuse exactly the kind of access datafood_bundle gives an agent. Each links to the full case and the policy that stops it:

Browse the full MCP Attack Database →

Every attack above starts with a tool call. PolicyLayer checks each one against your policy first, so datafood_bundle only ever does what you allow.

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Other read tools across the catalogue. The same approach applies to each: allow, with a rate cap to control cost.

What does the datafood_bundle tool do? +

Bundle 1-20 cross-niche queries in one call. Saves 50-92% vs. per-API. Free preview accepts up to 5; paid via Stripe session_id or x402 X-Payment header.. It is categorised as a Read tool in the Datafood Mcp MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on datafood_bundle? +

Register the Datafood MCP server in PolicyLayer and add a rule for datafood_bundle: 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 Datafood Mcp. Nothing to install.

What risk level is datafood_bundle? +

datafood_bundle is a Read tool with low risk. Read-only tools are generally safe to allow by default.

Can I rate-limit datafood_bundle? +

Yes. Add a rate_limit block to the datafood_bundle 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.

How do I block datafood_bundle completely? +

Set action: deny in the PolicyLayer policy for datafood_bundle. 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.

What MCP server provides datafood_bundle? +

datafood_bundle is provided by the Datafood MCP server (https://toughlovesec.win/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Datafood Mcp tool call.

Deterministic rules across all 4 Datafood Mcp tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.

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4,600+ MCP servers and 31,000+ tools scanned and risk-classified.

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