Teradata MCP Server

# Teradata Agents with Flowise AgentFlow and Teradata MCP Server [](https://youtu.be/xkmslhg_ulU) [](https://youtu.be/aM01xOndsvk) [](./Teradata_visualized_Agents_V2.json) [](https://youtu.be/pYx0dn65Z2s) This repository provides a set of **Teradata Agents** designed to integrate seamlessly with **Flowise AgentFlow** using the **Teradata MCP Server.** These agents enable intelligent workflows that combine **Teradata’s data and vector capabilities** with **LLM-powered analytics** — helping you build scalable, AI-driven data applications. Before getting started, make sure both **Teradata MCP Server and Flowise** containers are running as described in the setup guide below. ### 📘 Setup Guide: Refer to [Flowise_with_Teradata_MCP](../../../docs/client_guide/Flowise_with_teradata_mcp_Guide.md) for detailed installation and configuration steps. --- ## 🚀 Available Teradata Agents for Flowise ### 🧠 Teradata Data Science Agent This agent template provides a complete **Flowise workflow** to interact with **Teradata** for data science–related use cases such as querying data, running analytics, and generating insights using LLMs. #### Template: [Teradata_Data_Science_Workflow_Agents_V2.json](./Teradata_Data_Science_Workflow_Agents_V2.json) #### Configuration Steps: 1. Import the JSON template into Flowise. 2. Configure your preferred LLM model and provide its credentials. 3. Save and deploy the workflow. **🎥 How-To Video**: Watch this step-by-step video tutorial — [Teradata Data Science Agent Setup](https://youtu.be/xkmslhg_ulU) --- ### 🧩 Teradata Vector Store RAG Agent This agent template provides a complete **Flowise workflow** to interact with the **Teradata Vector Store**. It supports **similarity search** and **retrieval-augmented generation (RAG) on vectorized data that already resides in Teradata**, enabling context-aware question-answering and semantic insights. #### Template: [Teradata_VectorStore_RAG_Agent_V2.json](./Teradata_VectorStore_RAG_Agent_V2.json) #### Configuration Steps: 1. Import the JSON template into Flowise. 2. Configure your preferred LLM model and provide its credentials. 3. Save and deploy the workflow. **🎥 How-To Video**: Watch this step-by-step video tutorial — [Teradata VectorStore RAG Agent Setup](https://youtu.be/aM01xOndsvk) --- ### 💼 Customer Lifetime Value (CLV) Demo Agent This demo agent showcases how **Flowise** and **Teradata MCP Server** can work together to calculate and visualize **Customer Lifetime Value (CLV)** using Teradata data. It demonstrates practical use of LLMs for analytics, insights generation, and storytelling on customer data. #### Template: [Teradata_Customer_Lifetime_Value_V2](./Customer_Lifetime_Value_V2.json) #### Configuration Steps: 1. Import the JSON template into Flowise. 2. Configure your preferred LLM model and provide its credentials. 3. Save and deploy the workflow. **🎥 How-To Video**: Watch this step-by-step video tutorial — [Customer Lifetime Value (CLV) Demo Agent](https://youtu.be/pYx0dn65Z2s) --- ### 📊 Teradata Visualization Agent This agent template demonstrates how to **visualize Teradata data** within a **Flowise workflow**. It enables users to generate various types of **plots and charts** (e.g., line, pie, polor, radar) directly from Teradata query results — turning data into interactive visual insights. #### Template: [Teradata_visualized_Agents_V2.json](./Teradata_visualized_Agents_V2.json) #### Configuration Steps: 1. Import the JSON template into Flowise. 2. Configure your preferred LLM model and provide its credentials. 3. Save and deploy the workflow.