Delete a specific model version from the registry. Irreversible — the version and its metadata cannot be recovered.
AI agents call delete_model_version to permanently remove resources in MLflow MCP Server — typically in cleanup and lifecycle workflows. It does its job in a single call, and there is no undo.
This tool irreversibly deletes a model version and its associated metadata, with no recovery option. Deletion of versioned artifacts falls under the Destructive category, which ranks above Write/Execute.
From the tool's definition Tool name is 'delete_model_version' and description explicitly states 'Delete a specific model version from the registry. Irreversible — the version and its metadata cannot be recovered.'
Documented attack patterns abuse exactly the kind of access delete_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 delete_model_version:
{
"version": "1",
"default": "deny",
"hide": [
"delete_model_version"
]
}delete_model_version disappears from the agent's tool list entirely, and any attempt to call it is denied. The rest of the server keeps working.
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Delete a specific model version from the registry. Irreversible — the version and its metadata cannot be recovered. It is categorised as a Destructive tool in the MLflow MCP Server MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
Register the MLflow MCP Server MCP server in PolicyLayer and add a rule for delete_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.
delete_model_version is a Destructive tool with critical risk. Critical-risk tools should be blocked by default and only enabled with explicit human approval.
Yes. Add a rate_limit block to the delete_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 delete_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.
delete_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.
Free to start. No card required.
40 MLflow MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.