create_project | PolicyLayer

WHAT IT DOES

What create_project does on Cloudera Machine Learning (CML) MCP Server

AI agents use create_project to create or update resources in Cloudera Machine Learning (CML) MCP Server — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Cloudera Machine Learning (CML) MCP Server environment.

WHY IT NEEDS A POLICY

Why create_project needs a policy

Creating a project is a reversible Write operation that establishes a new resource in the CML system. While it modifies system state, it does not delete data, execute arbitrary code, or move money. Severity is medium because an unauthorized project creation could consume resources and potentially serve as a staging point for further misuse, but the action itself is recoverable.

From the tool's definition Tool name 'create_project' indicates creation of a new project resource in Cloudera Machine Learning. No description provided, but the verb 'create' and sibling tools like 'create_job', 'create_job_from_file', and 'patch_file' (all Write operations) confirm…

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

create_project Rate-limited

create_project 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.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "create_project": {
      "limits": [
        {
          "counter": "create_project_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

LIMIT THIS TOOL →

Instant setup, no code required.

PolicyLayer is an MCP gateway: every tool call is checked against your rule before it reaches Cloudera Machine Learning (CML) MCP Server, so you can run agents against it safely.

FAQ

Questions about create_project

What does the create_project tool do? +

create_project. It is categorised as a Write tool in the Cloudera Machine Learning (CML) MCP Server MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on create_project? +

Register the Cloudera Machine Learning (CML) MCP Server MCP server in PolicyLayer and add a rule for create_project: 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 Cloudera Machine Learning (CML) MCP Server. Nothing to install.

What risk level is create_project? +

create_project is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit create_project? +

Yes. Add a rate_limit block to the create_project 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 create_project completely? +

Set action: deny in the PolicyLayer policy for create_project. 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 create_project? +

create_project is provided by the Cloudera Machine Learning (CML) MCP Server MCP server (yw449/cloudera-cml-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.