Wexa AI
  1. Connectors and MCP Servers
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  1. Connectors and MCP Servers

Adding an MCP Server

MCP (Model Context Protocol) Servers let you plug custom services into your Wexa AI coworkers so agents can call bespoke logic, internal APIs, or proprietary models. Below is the author-ready content adapted for the wexa_sdk documentation.

1. Configure MCP Server (image 17.png)#

From the MCP Server configuration panel in Wexa Studio, supply the following fields:
Server Name – human-friendly label (e.g., Custom Data Processor)
Server Category – tag or grouping (e.g., analytics, data_processing)
Server Params – JSON/structured config (host, port, timeouts, etc.)
Command – executable that boots the server (python main.py, node server.js, docker run …)
Arguments – optional CLI args (["--mode", "production"])
Environment Variables – key/value secrets, API keys, database URLs
Server Logo – optional URL/icon for quick identification
SDK Example
Tip: Keep configs version-controlled. Treat secrets in environment_variables like any other credential.

2. Register & Deploy (images 19.png & 20.png)#

After completing the form, click Add Server to register and deploy the MCP service. Once deployment succeeds, the server appears in your workspace and can be referenced by agents or process flows.
Using the MCP Server in an Agent
Your MCP server is now part of the automation stack—agents can invoke it whenever their workflow requires custom logic or on-premise integrations.
Modified at 2026-01-02 10:05:30
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Adding a New Connector
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Setting up Connector Triggers