Teradata MCP Server

# Teradata MCP server in Flowise > **📍 Navigation:** [Documentation Home](../README.md) | [Server Guide](../README.md#-server-guide) | [Getting started](../server_guide/GETTING_STARTED.md) | [Architecture](../server_guide/ARCHITECTURE.md) | [Installation](../server_guide/INSTALLATION.md) | [Configuration](../server_guide/CONFIGURATION.md) | [Security](../server_guide/SECURITY.md) | [Customization](../server_guide/CUSTOMIZING.md) | [Client Guide](CLIENT_GUIDE.md) 1. **Make sure you have Teradata database access.** (the most convenient way: Go to https://clearscape.teradata.com create account and login, start the environment and click on Run Demo) 2. **Build Teradata mcp server container image** from https://github.com/Teradata/teradata-mcp-server, run below lines in cmd terminal. ``` git clone https://github.com/Teradata/teradata-mcp-server.git cd teradata-mcp-server # build container from Source code docker build --build-arg ENABLE_FS_MODULE=true \ --build-arg ENABLE_TDML_MODULE=true \ --build-arg ENABLE_TDVS_MODULE=true \ -t teradata-mcp-server:latest . ``` 3. **Build Flowise Container Image** from https://github.com/FlowiseAI/Flowise, run below lines in cmd terminal. ``` git clone https://github.com/FlowiseAI/Flowise.git cd Flowise docker build --no-cache -t flowise:latest . ``` 4. **Create Common .env file** for teradata-mcp-server and flowise container, ``` mkdir ~/td_ai_stack cd ~/td_ai_stack vi .env ``` ``` # ----------- MCP server and Database Env variables ------------# DATABASE_URI=teradata://username:password@host:1025/databasename LOGMECH=TD2 #TD2 or LDAP TD_POOL_SIZE=5 TD_MAX_OVERFLOW=10 TDPOOL_TIMEOUT=30 PROFILE=dataScientist DATABASE_HOST=IP_OF_DB_NODE MCP_TRANSPORT=streamable-http #stdio, sse, streamable-http MCP_HOST=0.0.0.0 MCP_PORT=8001 MCP_PATH=/mcp/ # ----- Enterprise Vector Store ---------- TD_BASE_URL=https://host/api/accounts/40c83ff23b2e #Your UES_URI, strip off the trailing /open-analytics #TD_PAT=gwxhQG2UZcDqQlp9LKWjEBfXB7 #Your PAT if you have Teradata Lake system. TD_PEM=/root/td_ai_stack/demo_key.pem #Your PEM with full path where you kept on host VS_NAME=vs_example #Your target Vector Store Name # ------------ Flowise env varieable -------------------# PORT=3000 CORS_ORIGINS=* IFRAME_ORIGINS=* DATA_DIR=~/td_ai_stack/.flowise # host dir to persist data of flowise ``` 5. **Create docker-compose.yaml file** to up teradata-mcp-server and flowise containers ``` cd ~/td_ai_stack vi docker-compose.yaml ``` ``` services: flowise: image: flowise:latest restart: always environment: - PORT=${PORT} # LOGGING - DEBUG=${DEBUG} # SETTINGS - CORS_ORIGINS=${CORS_ORIGINS} - IFRAME_ORIGINS=${IFRAME_ORIGINS} # Default Teradata Configuration env to refer into flowise - TD_MCP_SERVER=http://teradata-mcp-server:8001/mcp ports: - "${PORT}:${PORT}" extra_hosts: - "dbccop1:${DATABASE_HOST}" container_name: flowise healthcheck: test: ['CMD', 'curl', '-f', 'http://localhost:${PORT}/api/v1/ping'] interval: 10s timeout: 5s retries: 5 start_period: 30s volumes: - ${DATA_DIR}/.flowise:/root/.flowise teradata-mcp-server: image: teradata-mcp-server:latest restart: always environment: - DATABASE_URI=${DATABASE_URI} - LOGMECH=${LOGMECH} - MCP_TRANSPORT=${MCP_TRANSPORT} - MCP_PATH=${MCP_PATH} - MCP_HOST=${MCP_HOST} - MCP_PORT=${MCP_PORT} - PROFILE=${PROFILE} - TD_BASE_URL=${TD_BASE_URL} - TD_PAT=${TD_PAT} - TD_PEM=${TD_PEM} - VS_NAME=${VS_NAME} container_name: teradata-mcp-server extra_hosts: - "dbccop1:${DATABASE_HOST}" ports: - "${MCP_PORT}:${MCP_PORT}" volumes: - ${TD_PEM}:${TD_PEM} tty: true networks: default: name: td-ai-stack external: false ``` 6. **Up teradata MCP server and flowise container** ``` cd ~/td_ai_stack mkdir ~/td_ai_stack/.flowise docker image ls # make sure teradata-mcp-server and flowise container images are available docker compose up -d --remove-orphans ``` 7. **Validate docker container status** ``` docker ps # teradata-mcp-server container logs docker logs teradata-mcp-server -f # Flowise Container logs docker logs flowise -f ``` 8. **Login to flowise** http://IP:3000 or http://127.0.0.1:3000 first time login - Complete organization setup (set any username and password)  9. **How to configure Teradata MCP server into Flowise Agentflow** - 9.1. Go Into Agentflows menu and Add new  - 9.2. Drag and Drop Agent and connect it with Start Node   - 9.3. Set up LLM credentials and LLM for Agent - Double click on Agent-0 , it will show Model to select model from various provider - select provider like Azure ChatOpenAI and set Azure ChatOpenAI Parameters, - for connect Credentails select -Create New-, fill details and Add    - 9.4. Add Teradata MCP server as custom MCP server for Tools - Click on Add Tools - Select Custom MCP server - Setup - Custom MCP Server Parameters ``` { "url": "http://teradata-mcp-server:8001/mcp", } ``` - Refresh Button of Available Actions - Click on Drop down to select tools for Agent   10. **Save Agentflow with anyName**  11. **Execute AgentFlow**