What can you do with this server?

The Strands Agent MCP server enables you to: * List all available Strands agents registered with the server * Execute a specific Strands agent by providing its name and a text prompt

Which integrations are available for this server?

Integrates with [Amazon](/mcp/servers/integrations/amazon) Q, allowing for execution of Strands agents through Amazon Q's interface. Users can list available agents and execute them through commands in the Amazon Q chat.

Strands Agent MCP

A Model Context Protocol (MCP) server for executing Strands agents. This project provides a simple way to integrate Strands agents with Amazon Q and other MCP-compatible systems.

IMPORTANT: This project is currently in alpha stage and not yet published on PyPI.

Overview

Strands Agent MCP is a bridge between the Strands agent framework and the Model Context Protocol (MCP). It allows you to:

Register Strands agents as MCP tools
Execute Strands agents through MCP
Find agents by specific skills

The project uses a plugin architecture that makes it easy to add new agents without modifying the core code.

Related MCP server: Elasticsearch MCP Server

Installation

Note: This package is not yet available on PyPI. You'll need to install it from source.

# Clone the repository git clone https://github.com/yourusername/strands-agent-mcp.git cd strands-agent-mcp # Install the package pip install -e .

Usage

Starting the MCP Server

strands-agent-mcp

This will start the MCP server.

Environment Variables

The server supports the following environment variables:

PLUGIN_PATH: Custom path to look for plugins (default: ".")
PLUGIN_NAMESPACE: Custom namespace prefix for plugins (default: 'sap_mcp_plugin')

Creating Agent Plugins

To create a new agent plugin, create a Python package with a name that starts with sap_mcp_plugin_ (sap stands for strands agent plugin). Your package should implement a build_agents function that returns a list of AgentEntry objects:

from typing import List from boto3 import Session from strands import Agent from strands.models import BedrockModel from strands_agent_mcp.registry import AgentEntry def build_agents() -> List[AgentEntry]: return [ AgentEntry( name="my-agent", agent=Agent( model=BedrockModel(boto_session=Session(region_name="us-west-2")) ), skills=["general-knowledge", "coding"] ) ]

Using with Amazon Q

Once the MCP server is running, you can connect it to Amazon Q. Refer to the Amazon Q documentation for the correct connection parameters.

The following MCP tools will be available:

execute_agent: Execute an agent with parameters agent_name and prompt
list_agents: List all available agents

Architecture

The project consists of three main components:

Server: The MCP server that exposes the agent execution API
Registry: A registry for managing available agents and their skills
Plugins: Dynamically discovered modules that register agents with the registry

The server automatically discovers all installed plugins that follow the naming convention and registers their agents.

Dependencies

fastmcp>=2.3.4: For implementing the MCP server
strands-agents>=0.1.1: The core Strands agent framework
strands-agents-builder>=0.1.0: Tools for building Strands agents
strands-agents-tools>=0.1.0: Additional tools for Strands agents

Development

This project uses uv for dependency management. To set up a development environment:

Clone the repository
Install uv if you don't have it already: pip install uv
Create a virtual environment and install dependencies:
uv venv uv sync

Sample Plugin

The repository includes a sample plugin (sap_mcp_plugin_simple) that demonstrates how to create and register a simple agent:

from typing import List from boto3 import Session from strands import Agent from strands.models import BedrockModel from strands_agent_mcp.registry import AgentEntry def build_agents() -> List[AgentEntry]: return [ AgentEntry( name="simple-agent", agent=Agent( model=BedrockModel(boto_session=Session(region_name="us-west-2")) ), skills=["general-knowledge"] ) ]