About Teradata MCP Server Template
Teradata MCP Server Template is a community-developed MCP server that connects AI agents to Teradata databases for querying, analysis and administration. It targets data engineers and developers who want to define custom semantic layers.
Key features
- 01Explore tables and analyze data without SQL
- 02Define semantic layers and tools in YAML
- 03Tools for semantic search and vector storage
- 04Database administration and security tools
- 05Runs as CLI, HTTP server or Docker container
Use cases
- 01Exploring and analyzing Teradata data from Claude Desktop
- 02Developing data engineering agents in VS Code
- 03Building visual agent workflows with Flowise
MCP support
The project is an MCP server that exposes Teradata database tools, prompts and resources to MCP clients such as Claude Desktop and VS Code.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project README
Teradata MCP Server
Connect AI agents directly to Teradata with enterprise security and extensibility.

Quick Start (Choose Your Path)
| Client | Best For | Setup Time |
|---|---|---|
| Claude Desktop | Exploratory analysis, platform admin | 5 min |
| VS Code + Copilot | Data engineering, agent development | 5 min |
| Open WebUI | Testing new LLMs locally | 5 min |
| Code Examples | Build your own client | varies |
| Flowise | Visual agent builder | 10 min |
Pre-requisites: Teradata database (or free sandbox) + uv
Claude Desktop Setup (No Installation)
Add this to claude_desktop_config.json (Settings > Developer > Edit Config):
{
"mcpServers": {
"teradata": {
"command": "uvx",
"args": ["teradata-mcp-server"],
"env": {
"DATABASE_URI": "teradata://<USERNAME>:<PASSWORD>@<HOST_URL>:1025/<USERNAME>"
}
}
}
}
What You Can Do
| Use Case | Capabilities | Tools |
|---|---|---|
| Query & Analyze | Explore tables, profile data, explain results, visualize patterns—no SQL needed | base, dba, qlty, plot |
| Semantic Layer | Generate custom semantic layers and tools from YAML or with our Agent Skill | Learn more → |
| AI & RAG Pipelines | Semantic search, retrieval-augmented generation, vector storage | rag, tdvs, fs |
| Database Admin | Manage security, monitor capacity, automate backups | dba, sec, bar |
What's New (Latest Release)
- FastMCP v4 — Sessionless protocol, background tasks for long-running analytics, argument completion
- Response Caching — 5-minute TTL signals reduce redundant database queries in multi-turn conversations
- Guard Mode — Multi-step confirmation flows for destructive operations (bar_, sec_)
- Background Tasks —
tdml_*analytic functions return task IDs for polling instead of blocking - Argument Completion — Auto-suggest table and column names from schema as users type
- Hooks Capability — Intercept tool calls for custom monitoring, audit, or rate-limiting
- Row Limit Protection — Configurable caps (
DEFAULT_ROW_LIMIT,MAX_ROW_LIMIT) prevent LLM token overflow - Enhanced Security — VX views for fine-grained row-level access control
Extend & Deploy
Add Custom Logic
Use hooks to intercept tool calls for monitoring, audit trails, or validation → Hooks Guide
Define Semantic Layers
Create domain-specific tools, prompts, and cubes in YAML → Customization Guide
Deploy Everywhere
Run as CLI (uv), HTTP server, Docker container, or cloud service → Installation Guide
See It In Action
- Voice Agent — Real-time bidirectional audio with Amazon Nova Sonic
- Web Agent — Interactive chat UI with Google ADK framework
- Flowise Builder — Visual drag-and-drop workflows
- Custom Middleware — Performance monitoring patterns
Learn More
- Full Documentation — Installation, configuration, architecture, security
- Video Tutorials — Step-by-step walkthroughs
- Developer Guide — Extend and contribute
- Architecture — How components work together
Contributing
We welcome contributions! See our Contributing Guide and Developer Guide to get started.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.