request_workflow_deployment
Request hosted deployment for a workflow. Requires a prior dry-run id and keeps activation gated on policy pass and audit logging.
This record as markdown: /tools/adrata-starfield-mcp/request-workflow-deployment.md
What request_workflow_deployment does on Starfield
AI agents invoke request_workflow_deployment to trigger actions in Starfield. 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 |
|---|---|---|---|
path | string | — | Path to adrata.workflow.json on disk |
notes | string | — | |
target | string | — | |
dryRunId | string | Yes | Dry-run id returned by dry_run_workflow |
workflow | object | — | Inline workflow JSON; takes precedence over path |
Parameters from the server's own tool schema.
Why request_workflow_deployment is rated High
request_workflow_deployment triggers real processes with real consequences. An agent gone sideways doesn't fire it once. It starts dozens of builds, sends mass notifications, or burns through compute before anyone looks up.
Risk signalsAccepts file system path (path)
Attacks that exploit this kind of access
The rule that runs request_workflow_deployment safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Starfield, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For request_workflow_deployment, this is the rule to start with:
request_workflow_deployment 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 Starfield, apply this rule, and every request_workflow_deployment call is checked against it from then on.
Questions about request_workflow_deployment
Request hosted deployment for a workflow. Requires a prior dry-run id and keeps activation gated on policy pass and audit logging. It is categorised as a Execute tool in the Starfield MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
request_workflow_deployment accepts 5 parameters: path, notes, target, dryRunId, workflow. Required: dryRunId. The full parameter table on this page comes from the server's own tool schema.
Register the Starfield MCP server in PolicyLayer and add a rule for request_workflow_deployment: 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 Starfield. Nothing to install.
request_workflow_deployment 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 request_workflow_deployment 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_workflow_deployment. 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_workflow_deployment is provided by the Starfield MCP server (@adrata/starfield-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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