Get specific model version details (metrics, stage, run_id)
AI agents call get_model_version to retrieve information from MLflow 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 metadata about a model version—specifically metrics, deployment stage, and associated run ID. It performs a query operation without side effects. No data is created, modified, deleted, or irreversibly changed. While the MLflow server contains other destructive tools (delete_experiment, delete_model_version, etc.), this particular tool is clearly a read-only retrieval operation with minimal risk.
From the tool's definition Tool name 'get_model_version' and description 'Get specific model version details (metrics, stage, run_id)' indicate a retrieval operation with no modification or deletion of data.
Documented attack patterns abuse exactly the kind of access get_model_version gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and MLflow MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for get_model_version:
{
"version": "1",
"default": "deny",
"tools": {
"get_model_version": {}
}
}get_model_version is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Get specific model version details (metrics, stage, run_id). It is categorised as a Read tool in the MLflow MCP Server MCP Server, which means it retrieves data without modifying state.
Register the MLflow MCP Server MCP server in PolicyLayer and add a rule for get_model_version: 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 MLflow MCP Server. Nothing to install.
get_model_version 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 get_model_version 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 get_model_version. 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.
get_model_version is provided by the MLflow MCP Server MCP server (kkruglik/mlflow-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from MLflow MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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40 MLflow MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.