AI agents call follow_execution_logs to retrieve information from Kestra Python MCP Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
The name implies following (reading/tailing) execution logs, which is a read operation with no side effects. Confidence is reduced due to empty description, but the pattern of 'follow logs' consistently means read/stream in logging contexts. Sibling tools like 'download_execution_logs' and 'delete_execution_logs' suggest a log-management family where this tool retrieves log output.
From the tool's definition Tool name 'follow_execution_logs' suggests reading/streaming log data from an execution
Documented attack patterns abuse exactly the kind of access follow_execution_logs 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 follow_execution_logs:
{
"version": "1",
"default": "deny",
"tools": {
"follow_execution_logs": {}
}
}follow_execution_logs is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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follow_execution_logs. It is categorised as a Read tool in the Kestra Python MCP Server MCP Server, which means it retrieves data without modifying state.
Register the Kestra Python MCP Server MCP server in PolicyLayer and add a rule for follow_execution_logs: 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.
follow_execution_logs is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the follow_execution_logs 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 follow_execution_logs. 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.
follow_execution_logs 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.