MCP + Skills Framework: Guiding AI Agents for Efficient Data Science Workflows

A DevTalk on guiding AI agents (Claude, GPT) to operate correctly within a specific data platform, using an MCP server + skills framework. The core problem: agents are good at figuring out what to do in a data science workflow, but poor at choosing how to do it efficiently on a real data platform.

Common Agent Inefficiencies

• Generating client-heavy code instead of pushing work down to the database
• Moving more data/tokens than needed
• Ignoring native capabilities (analytics functions, ML, etc.)
• Falling back to generic patterns that don't scale

Solution: MCP Server + Skills Framework

Instead of letting the agent “figure it out,” constrain and guide it with platform-aware context. The approach focuses on:

• Selecting the right analytic functions
• Knowing when SQL isn't enough
• Using in-database ML / stats / text / vector operations
• Chaining everything into end-to-end workflows that are actually deployable

Resources

• Repo: github.com/ksturgeon-td/tdsql-mcp
• Free environment to try it: Teradata ClearScape Analytics Demo
• Live session recording: YouTube

If you're experimenting with Claude + MCP or tool use and have hit inefficiency or hallucination issues with real data systems, this approach is worth exploring.