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).
This record as markdown: /tools/io-github-getgapup-mcp-knowledge/job-result.md
What job_result does on Mcp Knowledge
AI agents call job_result to retrieve information from Mcp Knowledge without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
| Parameter | Type | Required | Description |
|---|---|---|---|
job_id | string | Yes | The job_id returned by an async tool call |
Parameters from the server's own tool schema.
Why job_result is rated Low
This tool only retrieves/queries the status and result of a previously submitted async job. It has no side effects of its own — it reads and returns data without creating, modifying, executing, or deleting anything. Misuse potential is minimal since it only exposes already-computed results.
From the tool's definition 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
Attacks that exploit this kind of access
The rule that runs job_result safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Mcp Knowledge, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For job_result, this is the rule to start with:
job_result is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Mcp Knowledge, apply this rule, and every job_result call is checked against it from then on.
Questions about 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). It is categorised as a Read tool in the Mcp Knowledge MCP Server, which means it retrieves data without modifying state.
job_result accepts 1 parameter: job_id. Required: job_id. The full parameter table on this page comes from the server's own tool schema.
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.
job_result 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 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.
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.
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.
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