Build a Model Context Protocol (MCP) Server on S3 using Lambda, Temporal, Ray, and Qdrant

Learn how to build an MCP (Multimodal Context Protocol) pipeline directly on top of S3 using Lambda (for change detection), Temporal (for orchestration), Ray (for scalable compute), and Qdrant (for vector search).

🧩 Architecture Overview

To build a fully event-driven MCP pipeline that indexes unstructured content from S3 (videos, PDFs, images, logs), here’s what you’ll need:

Amazon S3 — your raw unstructured data source
AWS Lambda — triggers feature extraction on new data (CDC style)
Temporal — orchestration and retries across modalities
Ray — distributed execution for compute-heavy tasks (e.g., video segmentation, embedding)
Qdrant — vector DB to store indexed representations

📥 Example: From Upload to Retrieval

Once this pipeline is in place, the flow becomes simple:

Upload a file (e.g. customer-incident.mp4) to S3
Trigger runs via Lambda → Temporal → Ray to extract embeddings
Store outputs in Qdrant (vectors) and Postgres (metadata)

Query Qdrant with a prompt like:

“Show me all videos where someone slips and falls indoors”

The system surfaces relevant clips — no manual tagging required.

🔗 System Flow

Change Detection (CDC): S3 → EventBridge → Lambda
Orchestration: Lambda kicks off a Temporal workflow
Distributed Processing: Temporal starts Ray tasks
Indexing: Outputs pushed to Qdrant (for vector search) and optionally Postgres (for metadata)

graph TD A[S3 - New File] --> B[EventBridge Trigger] B --> C[AWS Lambda - CDC] C --> D[Temporal Workflow - Orchestration] D --> E[Ray Tasks - Feature Extraction] E --> F[Qdrant - Vector Index] E --> G[Postgres - Metadata Store]

⚙️ Infrastructure Breakdown

Step 1: S3 CDC via Lambda

def lambda_handler(event, context):
    s3_key = event["Records"][0]["s3"]["object"]["key"]
    # Start Temporal workflow
    temporal_client.start_workflow("ProcessMultimodalFile", args={"key": s3_key})

Step 2: Temporal Workflow

@workflow.defn
class ProcessMultimodalFile:
    @workflow.run
    async def run(self, key):
        video_path = await download_from_s3(key)
        await workflow.execute_activity(extract_and_embed, video_path)

Step 3: Ray Tasks for Feature Extraction

@ray.remote
def extract_and_embed(path):
    vision = vision_embedder(path)
    audio = audio_embedder(path)
    qdrant_client.insert([vision, audio])

🧠 What’s Indexed?

Modality	Examples Extracted
Vision	Object detection, OCR, visual style
Audio	Speech-to-text, speaker ID
Text	Entities, sentiment, topic modeling
PDF/Image	Layouts, diagrams, handwriting

🏗️ Mixpeek’s Fully Managed MCP Stack

🔥 Long-Tail Use Cases

Industry	Use Case Example
Insurance	Detect slip-and-fall claims via video ingestion from branch cameras
Healthcare	Search for similar MRI results across a decade of unstructured imaging files
Education	Auto-index and summarize lecture videos for semantic search
Security	Tag suspicious behavior patterns across thousands of archived CCTV feeds
Media	Find moments of laughter or applause in podcast audio archives
Logistics	Scan forklift usage across warehouse footage to predict operator burnout

🧩 Skip the Plumbing

You could glue this all together — or you can use Mixpeek, which handles:

Multimodal ingestion pipelines
Zero-config embedding + indexing
Query-ready APIs for search, alerts, and retrieval
Event-based workflows with zero devops

Built for developers who don’t want to reinvent multimodal infra.