This article explains how to view and use logs related to AI agents and Guardrails in your environment. When you use AI agents and guardrails, you can track exactly what happened during a request...
Tool input defines the structure and format of data exchanged between systems. It acts as a blueprint, ensuring consistency by specifying required fields, data types, and validation rules. This re...
After you create an MCP server, you manage it from the MCP servers list in AI Studio. From there, you can update its tools and settings, test it, monitor the requests it receives, review configurat...
You can enter mock response data when configuring imports instead of executing a call to retrieve live response data from an import step. The mock response data that you provide for an import appl...
Use MCP server request logs to monitor activity, troubleshoot failed requests, and review execution details. Depending on the configured log mode, you can view request and response data and inspect...
End users are a lightweight user type for people who consume MCP tools through AI clients (Claude, Cursor, and other MCP-compatible clients) but don't need access to integrator.io. They authenticat...
You can connect one or more MCP servers to an AI agent to extend its capabilities. Once connected, the agent can use the tools, resources, and prompts exposed by the selected MCP server at runtime...
Tool output defines the data your tool returns after it runs. It determines what information is passed to the next step or back to the calling system. Configure your output to ensure the returned ...
Important Looking for the latest updates? Access the most recent agentic automation release notes in the Current release and Previous releases sections. AI automation 2026.5.1 release notes ...
This article answers common questions about execution logs and helps you understand quickly how to use them to monitor, trace, and troubleshoot your flows. What are execution logs? Execution log...