AWS Strands Agents: Building and Connecting Your First Model Context Protocol (MCP) Server
About
Model Context Protocol (MCP) Server and AWS Strands Agents Demonstration
Details
- Author
- garystafford
- GitHub stars
- 5
- Downloads
- 228
- Categories
- Cloud Service
Jump to
- Integrates with Shutterstock API for stock photography search.
- Integrates with National Weather Service (NWS) API for weather data.
- Uses Strands Agents code‑first framework for production AI agents.
- Supports both STDIO and Streamable HTTP MCP transports.
- Contextually selects relevant photos based on current weather conditions.
- Orchestrates multiple agents with reasoning and context‑aware automation.
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
AWS Strands Agents: Building and Connecting Your First Model Context Protocol (MCP) ServerCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Clone the repository, install Node.js packages with yarn install, set environment variables (API_KEY and SHUTTERSTOCK_API_TOKEN), start the MCP server (either node mcp-server.js for STDIO or node mcp-server-remote.js for Streamable HTTP transport), create a Python virtual environment, install Python dependencies, and run the agent with python agent_stdio_multi_agent.py.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"aws strands agents: building and connecting your first model context protocol (mcp) server": {
"aws-strands-agents-mcp-demo": {
"command": "node",
"args": [
"mcp-server.js"
]
}
}
}
}
McpServers
{
"aws-strands-agents-mcp-demo": {
"command": "node",
"args": [
"mcp-server.js"
]
}
}
AWS Strands Agents: Building and Connecting Your First Model Context Protocol (MCP) Server
Overview
Deploy an MCP server that searches for stock photography based on your current location and conditions using third-party APIs, and orchestrate it with Strands Agents and the Amazon Q Developer CLI.
The Model Context Protocol (MCP) provides a standardized interface that enables AI agents to interact seamlessly with external services. In this post, we’ll demonstrate how to build an MCP server that integrates with Shutterstock — my favorite platform for high-quality licensed images, videos, music, and creative tools — using their robust API. We’ll then show how to expose these rich media search capabilities to agents developed with Strands Agents, AWS’s code-first framework for building production-ready AI agents. By orchestrating multiple agents, we’ll enable them to access the National Weather Service (NWS) API, intelligently select contextually relevant photos based on current weather conditions, and deliver results that showcase the power of reasoning and context-aware automation.
JavaScript/Node.js MCP Servers based on: https://github.com/lucianoayres/mcp-server-node.
Prerequisites
Git distributed version control system
Node.js v22.16.01 LTS or newer (MCP servers)
npm, Yarn, or other Node.js package manager
Python 3.13.x (Strands Agents)
pip, Poetry, or another Python package manager
AWS Account (for access to Amazon Bedrock generative AI models; other model providers are also supported)
Free Shutterstock Test Account and Token (Register at Shutterstock Developers)
Optional: Amazon Q Developer CLI for last section of post
Installation
1. Clone Repository and Install Packages
npm install -g corepack # if using yarn
git clone https://github.com/garystafford/aws-strands-agents-mcp-demo.git
cd aws-strands-agents-mcp-demo
yarn install
2. Start MCP Servers
```bash
Sign in to leave a review
Use Google, GitHub, or an email account so ratings stay tied to real people.
No reviews posted yet.

