strands-agents-mcp

# 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. <a href="https://glama.ai/mcp/servers/@imgaray/strands-agents-mcp"> <img width="380" height="200" src="https://glama.ai/mcp/servers/@imgaray/strands-agents-mcp/badge" alt="Strands Agent MCP server" /> </a> **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. ```bash # 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 ```bash 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: ```python 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](https://github.com/astral-sh/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: ```bash 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: ```python 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.