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.
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'
Documented attack patterns abuse exactly the kind of access manage_executions 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 manage_executions:
{
"version": "1",
"default": "deny",
"tools": {
"manage_executions": {
"limits": [
{
"counter": "manage_executions_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}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.
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manage_executions. 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.
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.
manage_executions is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
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.
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.
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.
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.