replay_execution

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/replay-execution.md

WHAT IT DOES

What replay_execution does on Kestra Python MCP Server

AI agents invoke replay_execution 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 replay_execution is rated High

Based on the tool name and server context, 'replay_execution' most likely re-triggers or re-runs a previous workflow execution in Kestra, which constitutes executing an external operation. Sibling tools like 'execute_flow' and 'backfill_executions' confirm this server manages workflow execution operations. Description is empty, so confidence is reduced, but the name strongly implies triggering a workflow run.

From the tool's definition Tool name 'replay_execution' on a server that supports 'executions' operations; description is empty and uninformative.

Attacks that exploit this kind of access

RECOMMENDED RULE

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

replay_execution Rate-limited

replay_execution 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": {
    "replay_execution": {
      "limits": [
        {
          "counter": "replay_execution_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 replay_execution call is checked against it from then on.

FAQ

Questions about replay_execution

What does the replay_execution tool do? +

replay_execution 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 replay_execution? +

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

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

Can I rate-limit replay_execution? +

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

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

replay_execution 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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