AI agents invoke backfill_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.
A backfill operation in workflow orchestration systems like Kestra triggers re-execution of workflow runs for historical time periods. This falls under Execute as it initiates multiple workflow executions. The description is empty, reducing confidence, but the server context (workflow execution platform) and sibling tools (execute_flow, force_run_execution) strongly suggest this triggers bulk executions.
From the tool's definition Tool name 'backfill_executions' in context of a Kestra workflow server with sibling tools like 'execute_flow', 'force_run_execution'
Documented attack patterns abuse exactly the kind of access backfill_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 backfill_executions:
{
"version": "1",
"default": "deny",
"tools": {
"backfill_executions": {
"limits": [
{
"counter": "backfill_executions_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}backfill_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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backfill_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 backfill_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.
backfill_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 backfill_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 backfill_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.
backfill_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.