Medium Risk

job_result

Poll the result of any tool called with async:true. Returns status=pending while running, status=completed with the full result once done, status=failed on error, or status=not_found if the job_id is unknown or expired (TTL 24h).

Part of the Mcp Knowledge server.

job_result can modify Mcp Knowledge data, with no limits today. PolicyLayer puts allow, deny, and rate-limit rules on every call. Live in minutes.

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AI agents use job_result to create or modify resources in Mcp Knowledge. Write operations carry medium risk because an autonomous agent could trigger bulk unintended modifications. Rate limits prevent a single agent session from making hundreds of changes in rapid succession. Argument validation ensures the agent passes expected values.

Without a policy, an AI agent could call job_result repeatedly, creating or modifying resources faster than any human could review. PolicyLayer's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach Mcp Knowledge.

Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.

policy.json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "job_result": {
      "limits": [
        {
          "counter": "job_result_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

See the full Mcp Knowledge policy for all 271 tools.

Get this rule live on your own Mcp Knowledge 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 job_result 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 job_result only ever does what you allow.

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Other write tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.

What does the job_result tool do? +

Poll the result of any tool called with async:true. Returns status=pending while running, status=completed with the full result once done, status=failed on error, or status=not_found if the job_id is unknown or expired (TTL 24h).. It is categorised as a Write tool in the Mcp Knowledge MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on job_result? +

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

What risk level is job_result? +

job_result is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit job_result? +

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

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

job_result is provided by the Mcp Knowledge MCP server (https://mcp.gapup.io). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Mcp Knowledge tool call.

Deterministic rules across all 271 Mcp Knowledge tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.

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