MCP (Model Context Protocol) is becoming the standard for how AI agents interact with tools.
An MCP server lets AI agents discover and use real-world tools like databases, APIs, and cloud services.
In this guide, you’ll build a production-ready MCP server connected to AWS services — and deploy it using Bedrock AgentCore Runtime. :contentReference[oaicite:0]{index=0}
🧠 What is MCP (Model Context Protocol)?
MCP standardizes how AI agents interact with external tools.
- Agents ask what tools exist
- Server returns tool schemas
- Agent calls tools dynamically
- Server executes and returns results
👉 This removes custom integrations and makes tools reusable across agents.
⚙️ What We’re Building
A real MCP server with two tools:
- query_dynamodb → Query AWS DynamoDB
- get_s3_summary → List and summarize S3 data
Deployed on AWS Bedrock AgentCore → serverless + scalable.
🧪 Part 1: Build the MCP Server
pip install fastmcp boto3Core idea:
- Define tools using
@mcp.tool() - Use clear docstrings (this controls agent behavior)
- Return structured JSON always
👉 Tool descriptions are critical — they guide the agent.
🧪 Part 2: Test Locally
claude mcp add aws-tools -- python aws_mcp_server.pyTest by asking:
“How many files are in my S3 bucket?”
👉 Claude will automatically call your tool.
🚀 Part 3: Deploy to AWS Bedrock
Steps:
- Containerize with Docker
- Push to ECR
- Deploy via AgentCore Runtime
👉 Result:
- Serverless MCP execution
- Auto scaling
- Session isolation
🔁 Part 4: The Agentic Loop
The core pattern:
- Agent receives request
- Calls tool
- Gets result
- Repeats until done
👉 Controlled by stopReason internally.
🚨 3 Things That Break MCP in Production
1. Weak Tool Descriptions
If descriptions are vague, agents call wrong tools or fail.
2. Returning Too Much Data
Large responses overflow context → break reasoning.
3. Unhandled Exceptions
Always return structured JSON — never crash the loop.
⚡ New: Stateful MCP Sessions (2026)
Bedrock now supports session-based MCP servers:
- Elicitation → ask follow-up questions
- Sampling → generate content inside tools
- Progress updates → long-running tasks
👉 This enables real production-grade AI workflows.
🔗 Learn by Doing (Hands-On AI Labs)
👉 Build real MCP systems with validation and portfolio proof.
🔗 Related Articles
❓ FAQs
What is an MCP server?
An MCP server exposes tools that AI agents can discover and use dynamically.
Why use MCP with AWS?
It allows Bedrock agents to interact directly with AWS services like DynamoDB and S3.
Is MCP production-ready?
Yes, especially with Bedrock AgentCore Runtime and session-based execution.
What language is used for MCP servers?
Python is commonly used with frameworks like FastMCP.