AI agents use add_execution_labels to create or update resources in Kestra Python MCP Server — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Kestra Python MCP Server environment.
Adding labels is a write operation—it creates or modifies metadata on existing executions. It is reversible (labels can be removed or changed), so it is not Destructive. It does not execute arbitrary code (Execute), move money (Financial), or trigger external operations with open-ended side effects.
From the tool's definition Tool name 'add_execution_labels' indicates adding/attaching labels to executions. Labels are metadata that can be modified reversibly.
Documented attack patterns abuse exactly the kind of access add_execution_labels gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Kestra Python MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for add_execution_labels:
{
"version": "1",
"default": "deny",
"tools": {
"add_execution_labels": {
"limits": [
{
"counter": "add_execution_labels_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}add_execution_labels 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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add_execution_labels. It is categorised as a Write tool in the Kestra Python MCP Server MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Kestra Python MCP Server MCP server in PolicyLayer and add a rule for add_execution_labels: 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 Kestra Python MCP Server. Nothing to install.
add_execution_labels 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 add_execution_labels 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 add_execution_labels. 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.
add_execution_labels is provided by the Kestra Python MCP Server MCP server (kestra-io/mcp-server-python). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Kestra Python 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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39 Kestra Python MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.