add_execution_labels

A write tool on the Kestra Python MCP server.

SERVERKestra Python MCP Server SOURCEkestra-io/mcp-server-python

Medium RISK CLASS

Category Write

Parameters 00 required

Recommended Rate-limitedsee the rule below

Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/kestra-io-mcp-server-python/add-execution-labels.md

WHAT IT DOES

What add_execution_labels does on Kestra Python MCP Server

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.

RISK

Why add_execution_labels is rated Medium

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.

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs add_execution_labels safely

PolicyLayer is an MCP gateway: it sits between your AI agents and Kestra Python MCP Server, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For add_execution_labels, this is the rule to start with:

add_execution_labels Rate-limited

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.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "add_execution_labels": {
      "limits": [
        {
          "counter": "add_execution_labels_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

LIMIT THIS TOOL → Instant setup, no code required.

The button opens the PolicyLayer dashboard: create your workspace, connect Kestra Python MCP Server, apply this rule, and every add_execution_labels call is checked against it from then on.

FAQ

Questions about add_execution_labels

What does the add_execution_labels tool do? +

add_execution_labels is a write tool on the Kestra Python MCP Server MCP server. 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.

How do I enforce a policy on add_execution_labels? +

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.

What risk level is add_execution_labels? +

add_execution_labels is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit add_execution_labels? +

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.

How do I block add_execution_labels completely? +

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.

What MCP server provides add_execution_labels? +

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.

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