manage_executions

A execute tool on the Kestra Python MCP server.

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

High RISK CLASS

Category Execute

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/manage-executions.md

WHAT IT DOES

What manage_executions does on Kestra Python MCP Server

AI agents invoke manage_executions to trigger actions in Kestra Python MCP Server. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call: builds kicked off, notifications sent, workflows started.

RISK

Why manage_executions is rated High

The tool name 'manage_executions' implies operations on workflow executions. Given the server context (Kestra workflow management) and sibling tools that execute flows, change task states, and backfill executions, this tool likely triggers or modifies workflow executions, placing it in the Execute category.

From the tool's definition Tool name 'manage_executions' on a server described as supporting 'flow management, executions, backfills, and other Kestra features'; sibling tools include 'execute_flow', 'backfill_executions', 'change_taskrun_state'

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs manage_executions 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 manage_executions, this is the rule to start with:

manage_executions Rate-limited

manage_executions stays usable, but rate-capped: a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "manage_executions": {
      "limits": [
        {
          "counter": "manage_executions_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

RATE-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 manage_executions call is checked against it from then on.

FAQ

Questions about manage_executions

What does the manage_executions tool do? +

manage_executions is a execute tool on the Kestra Python MCP Server MCP server. It is categorised as a Execute tool in the Kestra Python MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

How do I enforce a policy on manage_executions? +

Register the Kestra Python MCP Server MCP server in PolicyLayer and add a rule for manage_executions: 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 manage_executions? +

manage_executions is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.

Can I rate-limit manage_executions? +

Yes. Add a rate_limit block to the manage_executions 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 manage_executions completely? +

Set action: deny in the PolicyLayer policy for manage_executions. 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 manage_executions? +

manage_executions 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.

KEEP EXPLORING

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