Sovereign Data Pipeline for AI Agents using Python & MCP

As AI agents gain the ability to call external tools, data pipelines must adapt to ensure that the data fed into these models is clean, privacy-compliant, and well-structured.

Connecting an AI agent directly to an unmonitored production database or raw API stream opens up risks ranging from PII leaks (violating GDPR or HIPAA) to system lockups under high-velocity webhooks.

To solve this, the SEOSiri Open-Source Research Initiative built etl-pipeline-mcp—a local-first Model Context Protocol (MCP) server engineered in Python.

Core Architectural Features

Dual-Tier Ingestion (Hot/Cold Memory Architecture):
High-speed events from webhooks (Stripe, GitHub, Shopify, HubSpot) are instantly written to an in-memory Hot Tier (:memory:) with built-in backpressure controls.
Automated PII Anonymization:
Before data is logged or moved to persistent storage, sensitive identifiers like email addresses and IP addresses undergo 256-bit SHA-256 hashing.
Multi-Source Identity Resolution (ID Stitching):
Disparate identifiers across different platforms are automatically stitched together into a single, permanent mcp_root_id in the database registry.
Warehouse & Data Lake Exports:
The server exposes tools for streaming clean records to Snowflake, ClickHouse, and BigQuery, as well as generating columnar Parquet buffers for DuckDB and S3.

Installation & Quickstart

You can install the package directly via PyPI:

pip install etl-pipeline-mcp

Or connect it to Claude Desktop or Cursor using uv:

{
"mcpServers": {
"seosiri-etl-pipeline": {
"command": "uv",
"args": [
"run",
"--github",
"SEOSiri-Official/etl-pipeline-mcp",
"src/main_server.py"
]
}
}
}