This article explains the different types of mappings available when working with tools and it focuses primarily on Tool output mapping. Understanding these differences will help you correctly str...
Input control defines how you pass data from a flow to an AI agent. You use it to build clear, reliable inputs from flow records, plain text, and supported files, while staying within model capabi...
Prerequisite: In Shopify, verify if the order status is fulfilled. When you run the NetSuite Fulfillment to Shopify Fulfillment Add flow, if the retry data has empty fulfillment_orders array and yo...
MCP resources are read-only files from Celigo file storage that you expose to AI agents through an MCP server. When an agent is connected to an MCP server with resources configured, it can list av...
After configuring your tool input and output, you can incorporate lookups and imports into your tool. Lookups allow your tool to dynamically reference and retrieve critical data from connected sys...
Output control determines how an AI agent returns data to your flow. Configure the output type to ensure the agent’s response is predictable, validated, and easy to map in downstream steps. You se...
MCP prompts are reusable, server-defined message templates that resolve into a sequence of chat messages. You can use prompts to standardize how the model receives instructions and reuse that patt...
In Tool Builder, you can add branching to your tools to simplify condition-based routing and reduce complexity when working with multiple applications or endpoints. Branching decreases your effort...
MCP resources are read-only files from Celigo file storage that an MCP server exposes to your agent. When you add resources to an agent, the agent can read those files at runtime to ground its dec...
A Celigo guardrail is an import-type flow step that evaluates each record against safety, compliance, or governance policies. For every record, it returns a structured JSON result with a determini...