job_complete
Mark the job as completed. This sanitizes PII from the context and records a completion summary. Use when all tasks in the job are done.
This record as markdown: /tools/io-github-saloprj-dialogbrain/job-complete.md
What job_complete does on Dialogbrain
AI agents use job_complete to create or update resources in Dialogbrain, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Dialogbrain environment.
| Parameter | Type | Required | Description |
|---|---|---|---|
job_id | integer | — | The ID of the job to complete |
summary | string | — | Brief summary of what was accomplished |
Parameters from the server's own tool schema.
Why job_complete is rated Medium
This tool creates or modifies data reversibly by updating job completion status and generating summary records. While it involves PII sanitization (a security-positive action), the core function is to write/record completion state to a job record. It is not destructive (data is retained, not deleted), not execute (no arbitrary code/commands triggered), and not financial.
From the tool's definition The tool description states it "Mark[s] the job as completed" and "records a completion summary," which are write operations that modify job state. The mention of sanitizing PII indicates data transformation and record updates.
Attacks that exploit this kind of access
The rule that runs job_complete safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Dialogbrain, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For job_complete, this is the rule to start with:
job_complete 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 Dialogbrain, apply this rule, and every job_complete call is checked against it from then on.
Questions about job_complete
Mark the job as completed. This sanitizes PII from the context and records a completion summary. Use when all tasks in the job are done. It is categorised as a Write tool in the Dialogbrain MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
job_complete accepts 2 parameters: job_id, summary. The full parameter table on this page comes from the server's own tool schema.
Register the Dialogbrain MCP server in PolicyLayer and add a rule for job_complete: 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 Dialogbrain. Nothing to install.
job_complete 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 job_complete 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_complete. 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_complete is provided by the Dialogbrain MCP server (https://api.dialogbrain.com/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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