Last updated: July 21, 2026
- We open-sourced
lambda-data-pipeline-mcpv1.0.0, a stateless MCP server that ingests high-volume data streams locally and exposes only clean, summarized, context-compliant payloads to AI clients. - It uses a decoupled Hot/Cold storage architecture: real-time webhooks land in an in-memory queue, heavy batch data goes straight to on-disk storage, avoiding both context overflow and memory exhaustion.
- Confirmed live via MCP Inspector: the server exposes exactly 6 tools, and a real call to
ingest_batch_api_pollwas executed against a live HubSpot-style payload and returned a successful result. - It's the fourth confirmed release in SEOSiri's MCP ecosystem, alongside the Bio-Robotics Core Engine, the API Guard, and the Learning Orchestrator.
What Does the Lambda Data Pipeline MCP Actually Do?
It sits between raw, high-volume business data — CMS webhooks, CRM records, social mentions — and an AI agent that can only usefully process small, structured payloads. Real-world systems produce data at a scale and shape that overflows an LLM's context window if fed directly; feeding gigabyte-scale datasets straight into a model results in context overflow, timeouts, or crashes. This server filters, summarizes, and structures that data locally first, so the AI client only ever receives clean, low-latency, context-compliant JSON.
To bridge this integration gap, we've open-sourced lambda-data-pipeline-mcp (v1.0.0). Built on the Model Context Protocol (MCP), it acts as a local-first analytical broker: it ingests, sanitizes, and prioritizes unstructured data locally, exposing only summarized, actionable segments to your AI client.
The Ingestion Architecture: Decoupled Hot & Cold Storage
To process both real-time webhooks and heavy historical datasets without exhausting system memory or adding latency, the server implements a hybrid, decoupled storage model:
- Hot Tier in-memory queue (RAM): a high-speed, serverless, in-memory SQLite instance. Real-time webhooks (CMS updates, ad-platform clicks, email tracking) write instantly to this queue. An active backpressure mechanism is documented to throttle incoming data if the queue exceeds 10,000 pending items, to prevent out-of-memory failures during event spikes.
- Cold Tier historical archive (Disk): a portable, on-disk SQLite database file (
cold_storage.db). Non-real-time batch data polled from external databases or CRM systems writes directly to disk, keeping RAM usage bounded regardless of historical data volume.
B2B Client Integrations: HubSpot and Salesforce Syncing
In high-volume B2B environments, feeding live CRM customer profiles and sales pipelines into an AI agent without violating privacy regulations or overloading its context window is a real, recurring problem. The server addresses this with two coordinated tasks:
A. Real-Time Webhook Routing
When a lead updates in HubSpot or a pipeline stage changes in Salesforce, the CRM broadcasts a webhook payload. The documented ingest_realtime_webhook tool captures this, validates authenticity via an HMAC-SHA256 signature, and stores the transaction in the Hot Tier instantly.
B. Unified Identity Resolution (ID Stitching)
A single lead can carry a HubSpot ID, a Salesforce ID, a social handle, and a personal email address across different systems. During the documented Lambda migration step (process_lambda_pipeline), the identity resolver merges these fragmented identifiers under a single mcp_root_id.
To align with privacy regulation intent under GDPR and the CCPA, personally identifiable information is documented to be hashed using SHA-256 (per NIST FIPS 180-4) before being written to the permanent Cold Storage disk, with raw email addresses and IP data redacted before that write.
Live-Tested: The Full 6-Tool Suite
Confirmed directly via Glama's browser-based MCP Inspector: the server responds with exactly 6 tools, and a real call to ingest_batch_api_poll was executed and returned a live result.
ingest_batch_api_poll processing a HubSpot lead record, with all six tools visible in the Inspector's tool list.The live test called ingest_batch_api_poll with crm_lead_id="hubspot_lead_99", email_address="[email protected]", and source_platform="hubspot". The server returned:
mcp_root_id: "7b524d3ee59d2c9b"— a short, hash-like unified identifier consistent with the documented identity-stitching behavior. Worth being precise about what this single response confirms: it shows a stitched ID was generated and the sync succeeded, not directly the SHA-256 PII-hashing step itself, since the raw email isn't echoed back in the response either way.source: "HUBSPOT"status: "BATCH_SYNC_SUCCESS"
One honest detail worth including rather than omitting: the request log for this session also shows an earlier connection attempt that failed with a Cloudflare 502 error before the successful call went through. That's a transient upstream connectivity issue, not a defect in the tool logic itself — the eventual call completed cleanly — but it's a real part of what the session showed, so it's reported here rather than left out.
Documented Tool Suite & Platform Compatibility
All six tools are documented below in detail. The tool list and one full call were independently confirmed live, as shown above; the remaining five tools' specific input/output shapes are reported from the project's own documentation, not individually re-tested in this session.
| Tool Name | Core Functionality | Arguments |
|---|---|---|
| ingest_realtime_webhook | Ingests high-velocity streaming events into the Hot memory queue with backpressure checks. | event_type, payload_json, source_platform, hmac_signature |
| ingest_batch_api_poll | Ingests batch data directly to on-disk Cold Storage, applying PII redaction and identity stitching. | crm_lead_id, email_address, payload_json, source_platform |
| process_lambda_pipeline | Migrates Hot Tier events to Cold Storage, applying ID stitching, anonymization, and priority scoring. | max_batch_size |
| export_to_data_warehouse | Exports anonymized, stitched, prioritized data to Snowflake, ClickHouse, or BigQuery. | target_warehouse, max_records |
| retrieve_analytical_summary | Queries and aggregates statistics across both Hot Tier (RAM) and Cold Tier (Disk). | subject_segment |
| sanitize_and_validate_payload | Validates payloads against OWASP rules and configurable compliance profiles. | proposed_payload, active_profiles_csv |
All six tools are documented as pure-Python with no external dependencies, targeting a minimal memory footprint across Windows, macOS, and Linux (Python 3.10+).
The SEOSiri MCP Ecosystem
This is the fourth confirmed release in SEOSiri's open-source MCP portfolio. Each server takes a domain with existing standards — robotics kinematics, API/compliance security, instructional design, and now data engineering — and builds a stateless MCP layer on top of it:
| Server | Domain | Live-Tested? |
|---|---|---|
| Bio-Robotics Core Engine | Robotics & kinematics | Yes — 6 tools, verified live outputs |
| API Guard | API & hardware-command security | Yes — 1 tool, verified live output |
| Learning Orchestrator | EdTech / skills development | Yes — 5 tools, verified live output |
| Lambda Data Pipeline | Data engineering / CRM ingestion | Yes — 6 tools, verified live output |
Developer's Guide: Zero-Setup Claude/Cursor Connection
The server is packaged in cross-platform Python, supporting remote execution via uv with no local install required:
{
"mcpServers": {
"seosiri-data-pipeline": {
"command": "uv",
"args": [
"run",
"--github",
"SEOSiri-Official/lambda-data-pipeline-mcp",
"src/main_server.py"
]
}
}
}
Save this inside your local claude_desktop_config.json, and Claude will pull, install, and execute the server directly from GitHub.
Query Answers
What is a Data Pipeline MCP server?
A local-first utility that acts as an analytical broker between raw data streams and AI clients, ingesting and summarizing big datasets locally so AI agents receive clean, low-latency, context-compliant payloads.
How does it manage big data without overloading AI context limits?
Via a two-tier Hot/Cold storage architecture — high-velocity events go to an in-memory Hot Tier, batch data goes to an on-disk Cold Tier, and only aggregated, summarized analytics are exposed to the AI client.
What is identity stitching?
The consolidation of fragmented user identifiers, such as CRM lead IDs, social handles, and email addresses, into a single unified ID, giving a coherent view of user interactions across systems.
How does it handle GDPR and CCPA compliance?
Documented behavior is to hash personally identifiable information using SHA-256 before writing records to permanent storage, with raw emails and IP addresses redacted first — consistent with, though not a substitute for, a full compliance program.
Has this tool suite been independently verified live?
Yes. A live MCP Inspector session confirmed all 6 tools, and a real execution of ingest_batch_api_poll against a HubSpot-style lead record returned a successful batch sync with a generated unified ID.
Is it open source?
Yes, under the MIT License, with source available on GitHub.
B2B Custom Solutions & Systems Integration Services
SEOSiri-Official offers systems-integration consulting for enterprise scaling across Linux, Windows, and macOS environments:
- Enterprise big data & warehouse exports: customizing webhook ingestion and identity-stitching feeds into Snowflake, ClickHouse, or BigQuery.
- Bespoke enterprise API security: custom compliance profiles inside on-premise container networks.
To discuss custom data deployments or compliance analytics, contact the architecture team:
Official Website: seosiri.com
Enterprise Support Email: [email protected]
Reference Sources
- Model Context Protocol Specification — modelcontextprotocol.io
- HubSpot Developers — Webhooks API
- Salesforce Developers — Pub/Sub API
- IETF — RFC 2104: HMAC
- NIST — FIPS 180-4 (SHA-256)
- EUR-Lex — GDPR, Official Text
- California Office of the Attorney General — CCPA
Explore or Contribute to the Project
The project is fully open-source. Explore the codebase, file an issue, or get involved in development directly on GitHub.
View on GitHub ➔ Sponsor the ProjectLead Architect & Open-Source Attribution
The systems architecture, storage design, and security interlocks presented across this ecosystem are designed and engineered by Momenul Ahmad, Lead Architect and Founder of SEOSiri.
All four confirmed SEOSiri MCP projects are developed under the official SEOSiri-Official open-source research initiative. To fund ongoing safety research, consider supporting the team via the SEOSiri GitHub Sponsors Portal.
Safety & Regulatory Note
This tool is a reference implementation suitable for prototyping and internal engineering use. Any deployment handling regulated personal data (health, financial, or otherwise) at production scale should undergo independent compliance and security review beyond the scope of this open-source repository.
