job_escalate
Escalate the job to a human. Use when you cannot resolve an issue, someone is not responding, or a situation requires human judgment.
This record as markdown: /tools/io-github-saloprj-dialogbrain/job-escalate.md
What job_escalate does on Dialogbrain
AI agents invoke job_escalate to trigger actions in Dialogbrain. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call: builds kicked off, notifications sent, workflows started.
| Parameter | Type | Required | Description |
|---|---|---|---|
job_id | integer | — | The ID of the job to escalate |
reason | string | Yes | Why escalation is needed |
Parameters from the server's own tool schema.
Why job_escalate is rated High
This tool triggers an external operation — routing/escalating a job to a human agent — which has real-world workflow effects that depend on context. It doesn't simply read or write data reversibly; it initiates a process handoff that can alter how conversations or tasks are handled.
From the tool's definition Escalate the job to a human. Use when you cannot resolve an issue, someone is not responding, or a situation requires human judgment.
Attacks that exploit this kind of access
The rule that runs job_escalate 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_escalate, this is the rule to start with:
job_escalate stays usable, but rate-capped: a runaway agent can't fire it dozens of times 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_escalate call is checked against it from then on.
Questions about job_escalate
Escalate the job to a human. Use when you cannot resolve an issue, someone is not responding, or a situation requires human judgment. It is categorised as a Execute tool in the Dialogbrain MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
job_escalate accepts 2 parameters: job_id, reason. Required: reason. 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_escalate: 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_escalate is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the job_escalate 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_escalate. 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_escalate 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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