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.
📖 Read the full source: r/ClaudeAI
👀 See Also
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