Set a tag on a run (e.g. annotate best model, flag for review).
AI agents use set_run_tag 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 creates or modifies tag metadata on an MLflow run, which is a reversible write operation. It does not execute code, delete data irreversibly, or commit financial actions. The blast radius is medium because incorrect tagging could mislead model selection or governance workflows, but the action is easily undone by removing the tag.
From the tool's definition Tool name 'set_run_tag' and description 'Set a tag on a run' indicate creation/modification of metadata. Tags are reversible annotations that modify run properties without deleting or executing external operations.
Documented attack patterns abuse exactly the kind of access set_run_tag 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 set_run_tag:
{
"version": "1",
"default": "deny",
"tools": {
"set_run_tag": {
"limits": [
{
"counter": "set_run_tag_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}set_run_tag 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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Set a tag on a run (e.g. annotate best model, flag for review). 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 set_run_tag: 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.
set_run_tag 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 set_run_tag 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 set_run_tag. 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.
set_run_tag 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.