Get detailed information about a specific run. Run data can be large — avoid fetching many runs at once.
AI agents call get_run 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 queries and retrieves metadata and data associated with a specific MLflow run. It is a read-only operation that does not create, modify, delete, or execute anything. The cautionary note about data size is operational guidance, not evidence of destructive or execute-class risk. Confidence is high because the verb 'Get' and explicit retrieval semantics are unambiguous.
From the tool's definition Tool description states 'Get detailed information about a specific run' — a retrieval operation with no side effects. The warning about run data being large is a performance note, not an indication of state-changing behavior.
Documented attack patterns abuse exactly the kind of access get_run 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_run:
{
"version": "1",
"default": "deny",
"tools": {
"get_run": {}
}
}get_run is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Get detailed information about a specific run. Run data can be large — avoid fetching many runs at once. 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_run: 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_run 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_run 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_run. 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_run 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.