Update the description of a model version.
AI agents use update_model_version to create or update resources in MLflow MCP Server — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your MLflow MCP Server environment.
This tool modifies existing data (a model version's description) in a way that can be undone or changed later, fitting the Write category. It is not destructive (the original data is not deleted), not financial, and not code execution.
From the tool's definition The tool description states 'Update the description of a model version', which modifies metadata associated with a model version without deleting or destroying data. This is a reversible modification operation.
Documented attack patterns abuse exactly the kind of access update_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 update_model_version:
{
"version": "1",
"default": "deny",
"tools": {
"update_model_version": {
"limits": [
{
"counter": "update_model_version_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}update_model_version 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.
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Update the description of a model version. It is categorised as a Write tool in the MLflow MCP Server MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the MLflow MCP Server MCP server in PolicyLayer and add a rule for update_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.
update_model_version is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the update_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 update_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.
update_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.