I built an open-source MCP Data Pipeline for AI Agents

Hey IH community,

I’ve been exploring how AI agents interact with real-world databases and APIs without exposing sensitive user information or hitting rate limits.

To solve this, I built and published etl-pipeline-mcp — a local-first Model Context Protocol (MCP) server that acts as a secure ETL/ELT data orchestrator.

What it does under the hood:

Hot/Cold Tiering: Streams real-time webhooks (Stripe, GitHub, HubSpot) into an in-memory SQLite buffer before Archiving to disk.

Automated Compliance: Hashes PII using SHA-256 at rest, fulfilling GDPR and HIPAA privacy requirements.

Identity Resolution: Stitches 5+ platform IDs (emails, CRM IDs, Stripe customer tokens) into a single mcp_root_id.

Data Warehouse Exports: Streams clean records to Snowflake, ClickHouse, or BigQuery, and exports Parquet buffers for DuckDB/S3.

It’s completely open-source (MIT), built on Python & FastMCP, and now published on PyPI (pip install etl-pipeline-mcp).

If you're building with AI agents or the Model Context Protocol, I'd love your feedback on the architecture!

Links:
PyPI Package: https://pypi.org/project/etl-pipeline-mcp/
GitHub Repository: https://github.com/SEOSiri-Official/etl-pipeline-mcp
Full Architecture Breakdown: https://www.seosiri.com/2026/07/etl-pipeline-mcp.html