Low Risk

submission_status

Public read. Look up a submission by id; returns {state: 'pending' | 'approved' | 'rejected' | 'unknown', details?}. Pending queue lives in process memory — dyno restart wipes it (resubmit if status becomes 'unknown' after a deploy).

Part of the AI Success Story server.

submission_status 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 submission_status to retrieve information from AI Success Story 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 submission_status 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": {
    "submission_status": {}
  }
}

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Get this rule live on your own AI Success Story 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 submission_status 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 submission_status 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 submission_status tool do? +

Public read. Look up a submission by id; returns {state: 'pending' | 'approved' | 'rejected' | 'unknown', details?}. Pending queue lives in process memory — dyno restart wipes it (resubmit if status becomes 'unknown' after a deploy).. It is categorised as a Read tool in the AI Success Story MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on submission_status? +

Register the AI Success Story MCP server in PolicyLayer and add a rule for submission_status: 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 AI Success Story. Nothing to install.

What risk level is submission_status? +

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

Can I rate-limit submission_status? +

Yes. Add a rate_limit block to the submission_status 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 submission_status completely? +

Set action: deny in the PolicyLayer policy for submission_status. 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 submission_status? +

submission_status is provided by the AI Success Story MCP server (https://ai-success-story-20f19ed7769b.herokuapp.com/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every AI Success Story tool call.

Deterministic rules across all 7 AI Success Story tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.

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