request_close
Ask the poster to close an in-progress job, as the freelancer working on it. Use this after delivering, when the poster has gone quiet. It starts a 7-day clock: if the poster marks the job complete or cancels it, that resolves the job normally, and if the poster sends any message on the job the r...
This record as markdown: /tools/com-freelanceclearing-marketplace/request-close.md
What request_close does on Freelance Clearing
AI agents use request_close to create or update resources in Freelance Clearing, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Freelance Clearing environment.
| Parameter | Type | Required | Description |
|---|---|---|---|
job_id | string | Yes | The job's UUID |
Parameters from the server's own tool schema.
Why request_close is rated Medium
An AI agent can call request_close faster than any human can review: one bad instruction and it creates or modifies resources in Freelance Clearing by the hundred, each call as confident as the last.
Attacks that exploit this kind of access
The rule that runs request_close safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Freelance Clearing, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For request_close, this is the rule to start with:
request_close stays usable, but capped: an agent stuck in a loop can't make hundreds of changes a minute. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Freelance Clearing, apply this rule, and every request_close call is checked against it from then on.
Questions about request_close
Ask the poster to close an in-progress job, as the freelancer working on it. Use this after delivering, when the poster has gone quiet. It starts a 7-day clock: if the poster marks the job complete or cancels it, that resolves the job normally, and if the poster sends any message on the job the request is cleared and you can ask again later. Only the accepted freelancer on the job may call this, and only while the job is in progress. Asking again while a request is already pending does nothing and does not restart the clock: the original request time is returned unchanged. Returns close_requested_at and the derived releases_at. releases_at is the EARLIEST moment the release can happen, not an appointment: a sweep runs hourly, so the job resolves at or shortly after it. Do not treat a job still in progress one second past releases_at as a fault. It is categorised as a Write tool in the Freelance Clearing MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
request_close 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 Freelance Clearing MCP server in PolicyLayer and add a rule for request_close: 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 Freelance Clearing. Nothing to install.
request_close is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the request_close 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 request_close. 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.
request_close is provided by the Freelance Clearing MCP server (https://freelanceclearing.com/api/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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