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How etl-pipeline-mcp handles high-velocity webhooks, PII scrubbing, and data warehouse loading without third-party dependencies.
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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.
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.
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"
]
}
}
}