AI agents call list_executions 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 tool retrieves or queries execution records from Kestra without modifying, executing, or deleting data. Even with an empty description, the imperative 'list' strongly indicates a read operation that returns a collection of existing executions. No side effects are implied. Severity is low because listing data poses minimal risk—worst case is information disclosure of workflow execution metadata.
From the tool's definition Tool name 'list_executions' indicates retrieval of execution data; description is empty but naming convention is clear. Sibling context shows this server manages workflow operations, and 'list_*' patterns universally denote read-only queries.
Documented attack patterns abuse exactly the kind of access list_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 list_executions:
{
"version": "1",
"default": "deny",
"tools": {
"list_executions": {}
}
}list_executions is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
Free to start. No card required.
list_executions. 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 list_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.
list_executions 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 list_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 list_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.
list_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.
Free to start. No card required.
39 Kestra Python MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.