follow_execution_logs

A read tool on the Kestra Python MCP server.

SERVERKestra Python MCP Server SOURCEkestra-io/mcp-server-python

Low RISK CLASS

Category Read

Parameters 00 required

Recommended Allowedsee the rule below

Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/kestra-io-mcp-server-python/follow-execution-logs.md

WHAT IT DOES

What follow_execution_logs does on Kestra Python MCP Server

AI agents call follow_execution_logs to retrieve information from Kestra Python MCP Server without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.

RISK

Why follow_execution_logs is rated Low

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

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs follow_execution_logs safely

PolicyLayer is an MCP gateway: it sits between your AI agents and Kestra Python MCP Server, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For follow_execution_logs, this is the rule to start with:

follow_execution_logs Allowed

follow_execution_logs is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "follow_execution_logs": {}
  }
}

ALLOW ONLY THIS TOOL → Instant setup, no code required.

The button opens the PolicyLayer dashboard: create your workspace, connect Kestra Python MCP Server, apply this rule, and every follow_execution_logs call is checked against it from then on.

FAQ

Questions about follow_execution_logs

What does the follow_execution_logs tool do? +

follow_execution_logs is a read tool on the Kestra Python MCP Server MCP server. It is categorised as a Read tool in the Kestra Python MCP Server MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on follow_execution_logs? +

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.

What risk level is follow_execution_logs? +

follow_execution_logs is a Read tool with low risk. Read-only tools are generally safe to allow by default.

Can I rate-limit follow_execution_logs? +

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.

How do I block follow_execution_logs completely? +

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.

What MCP server provides follow_execution_logs? +

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.

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