AI agents invoke render_trigger_deploy to trigger actions in UnClick. 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 |
|---|---|---|---|
api_key | string | — | |
service_id | string | Yes | |
clear_cache | boolean | — | Clear build cache before deploying |
Parameters from the server's own tool schema.
This tool triggers deployment operations on Render, an external hosting platform. While deployments themselves are not inherently destructive, they execute code in production and can cause service outages, data loss, or security issues depending on what is deployed. This makes it an Execute-category tool with high severity due to the potential blast radius of a misconfigured or malicious deployment.
From the tool's definition The tool description states it will "Trigger a new deploy for a Render service." This directly executes an external operation (deployment) whose effects are determined by the service configuration and cannot be easily undone if the deployment causes issues.
Risk signalsHandles credentials or secrets (api_key)
Attacks that exploit this kind of access
Trigger a new deploy for a Render service. It is categorised as a Execute tool in the UnClick MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
render_trigger_deploy accepts 3 parameters: api_key, service_id, clear_cache. Required: service_id. The full parameter table on this page comes from the server's own tool schema.
Register the UnClick MCP server in PolicyLayer and add a rule for render_trigger_deploy: 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 UnClick. Nothing to install.
render_trigger_deploy 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 render_trigger_deploy 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 render_trigger_deploy. 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.
render_trigger_deploy is provided by the UnClick MCP server (@unclick/mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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