AWS Strands Agents: Building and Connecting Your First Model Context Protocol (MCP) Server
LocalAbout AWS Strands Agents: Building and Connecting Your First Model Context Protocol (MCP) Server
A JavaScript/Node.js MCP server demo for developers exploring Strands Agents integrations. It searches Shutterstock images using location and weather context, and demonstrates agent orchestration with National Weather Service data.
Key features
- 01Search Shutterstock images by location and weather
- 02Retrieve weather data through the NWS API
- 03Orchestrate multiple agents with Strands Agents
- 04Support STDIO and Streamable HTTP transports
Use cases
- 01Build image searches informed by local weather
- 02Explore connecting Strands Agents to MCP servers
MCP support
The project implements an MCP server that exposes Shutterstock media search to clients. Its demonstration also shows Strands Agents connecting to the server and combining calls with weather data.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEAWS 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
- 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
- Start MCP Servers
# terminal window 1: STDIO transport
# environment variables are set by MCP server with STDIO
export API_KEY=abc-1234567890
export SHUTTERSTOCK_API_TOKEN=<YOUR_SHUTTERSTOCK_API_TOKEN>
node mcp-server.js
# terminal window 2: Streamable HTTP transport
# environmental variables are set by agent with Streamable HTTP
node mcp-server-remote.js
- Install Strands Agents
python -m pip install virtualenv -Uqqq
python -m venv .venv
source .venv/bin/activate
python -m pip install pip -Uqqq
python -m pip install -r requirements.txt -Uqqq
- Start Strands Agents
export API_KEY=abc-1234567890
export SHUTTERSTOCK_API_TOKEN=<YOUR_SHUTTERSTOCK_API_TOKEN>
python agent_stdio_multi_agent.py
The contents of this repository represent my viewpoints and not of my past or current employers, including Amazon Web Services (AWS). All third-party libraries, modules, plugins, and SDKs are the property of their respective owners.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.