tdsql-mcp

# tdsql-mcp An MCP server that turns Teradata Vantage into a full-stack analytics agent platform — giving AI agents not just SQL execution, but a structured, hierarchical knowledge base of Teradata's native function ecosystem. ## What This Is Most database MCP servers provide query execution. This one goes further: it equips agents with the knowledge they need to use Teradata *correctly and optimally* — not just to run arbitrary SQL, but to reach for the right native distributed function for each step of an analytics workflow. Teradata Vantage includes hundreds of built-in table operators for ML, statistics, data preparation, text analytics, and vector search. These run distributed across all AMPs in parallel and consistently outperform equivalent hand-written SQL. The challenge for agents is *discovery* — knowing these functions exist, knowing which one to use, and knowing how to combine them into pipelines. This server solves that with a structured syntax reference library and an agent guidance architecture that directs models toward native functions at every decision point. > **How does it work?** See [docs/architecture.md](docs/architecture.md) for a full explanation of the MCP server, Skill, and combined architectures — including diagrams of all three modes. --- ## Why This Matters The conventional pattern for AI-assisted analytics looks like this: ``` Agent pulls data → processes in Python / LLM context → returns result ``` This works at small scale but collapses under real-world conditions: the data transfer is expensive, the LLM context fills with raw data instead of insights, results are ephemeral and non-reproducible, and nothing produced is reusable at scale. The in-database approach enabled by this server inverts that model: ``` Agent orchestrates SQL → analytics execute on the platform → only results returned ``` ### Zero Data Movement Native Teradata table operators execute where the data lives — across all AMPs in parallel. No rows are…