AI agents call search_flows 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.
Search operations retrieve data without side effects. The naming convention aligns with typical read operations. Absence of description lowers confidence slightly, but the tool name itself is clear enough to classify as a Read operation with low blast radius if misused by an AI agent.
From the tool's definition Tool name 'search_flows' indicates a search/query operation. While the description is empty, the name and context of sibling tools (which include destructive operations like delete_execution_logs and delete_flow_logs) suggest this is a read-only query…
Documented attack patterns abuse exactly the kind of access search_flows 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 search_flows:
{
"version": "1",
"default": "deny",
"tools": {
"search_flows": {}
}
}search_flows is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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search_flows. 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 search_flows: 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.
search_flows 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 search_flows 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 search_flows. 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.
search_flows 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.