What is Strands Agents Mcp

Strands Agent MCP is a Model Context Protocol (MCP) server designed for executing Strands agents. It serves as a bridge between the Strands agent framework and MCP-compatible systems, enabling integration and execution of agents.

Use cases

Use cases for Strands Agent MCP include executing robotic tasks, managing agent interactions in automated systems, and integrating with platforms like Amazon Q for enhanced agent communication.

How to use

To use Strands Agent MCP, first install it using ‘pip install strands-agent-mcp’. Start the MCP server with ‘strands-agent-mcp’. You can create agent plugins by implementing a ‘register_plugin’ function in a Python package. Once the server is running, you can interact with agents using Amazon Q.

Key features

Key features of Strands Agent MCP include: registration of Strands agents as MCP tools, execution of agents through MCP, discovery and listing of available agents, and a plugin architecture that allows easy addition of new agents.

Where to use

Strands Agent MCP can be used in various fields such as robotics, artificial intelligence, and automation, where integration of agents into MCP-compatible systems is required.

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.

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:

1. Server: The MCP server that exposes the agent execution API
2. Registry: A registry for managing available agents and their skills
3. 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:

1. Clone the repository
2. Install uv if you don’t have it already: pip install uv
3. 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"]
        )
    ]

License

This project is licensed under the terms of the LICENSE file included in the repository.