request_deployment

SERVERAdrata SOURCE@adrata/adrata-mcp

High RISK CLASS

Category Execute

Parameters 00 required

Recommended Rate-limitedsee the rule below

Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/adrata-adrata-mcp/request-deployment.md

WHAT IT DOES

What request_deployment does on Adrata

AI agents invoke request_deployment to trigger actions in Adrata. 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.

RISK

Why request_deployment is rated High

request_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.

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs request_deployment safely

PolicyLayer is an MCP gateway: it sits between your AI agents and Adrata, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For request_deployment, this is the rule to start with:

request_deployment Rate-limited

request_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.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "request_deployment": {
      "limits": [
        {
          "counter": "request_deployment_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

RATE-LIMIT THIS TOOL → Instant setup, no code required.

The button opens the PolicyLayer dashboard: create your workspace, connect Adrata, apply this rule, and every request_deployment call is checked against it from then on.

FAQ

Questions about request_deployment

What does the request_deployment tool do? +

Request that the Adrata control plane deploy a custom integration or extension. The manifest is validated locally first; the request is only sent if validation passes. Activation stays gated on policy review. It is categorised as a Execute tool in the Adrata MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

How do I enforce a policy on request_deployment? +

Register the Adrata MCP server in PolicyLayer and add a rule for request_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 Adrata. Nothing to install.

What risk level is request_deployment? +

request_deployment is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.

Can I rate-limit request_deployment? +

Yes. Add a rate_limit block to the request_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.

How do I block request_deployment completely? +

Set action: deny in the PolicyLayer policy for request_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.

What MCP server provides request_deployment? +

request_deployment is provided by the Adrata MCP server (@adrata/adrata-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

KEEP EXPLORING

More on Adrata, and thousands of servers like it.

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http_request on 0sec → All high-risk MCP tools → The MCP Attack Database →

Guides

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