List all available runtime addons (e.g., Spark3, GPU, etc.).
AI agents call list_runtime_addons to retrieve information from Cloudera Machine Learning (CML) MCP Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool retrieves and displays information about available runtime addons without creating, modifying, deleting, or executing any operations. It is a purely informational read operation with minimal security impact—an AI agent misusing it could only learn what runtime options exist, with no blast radius for unauthorized access or system compromise.
From the tool's definition Tool name 'list_runtime_addons' and description 'List all available runtime addons' indicate a query/retrieval operation with no modification or side effects.
Attacks that exploit this kind of access
List all available runtime addons (e.g., Spark3, GPU, etc.). It is categorised as a Read tool in the Cloudera Machine Learning (CML) MCP Server MCP Server, which means it retrieves data without modifying state.
Register the Cloudera Machine Learning (CML) MCP Server MCP server in PolicyLayer and add a rule for list_runtime_addons: 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.
list_runtime_addons is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the list_runtime_addons 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 list_runtime_addons. 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.
list_runtime_addons 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.
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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