https://github.com/imgaray/strands-agents-mcp
An MCP server to execute agents through MCP
https://github.com/imgaray/strands-agents-mcp
Last synced: 3 months ago
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An MCP server to execute agents through MCP
- Host: GitHub
- URL: https://github.com/imgaray/strands-agents-mcp
- Owner: imgaray
- License: mit
- Created: 2025-05-18T00:24:58.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-05-18T00:36:25.000Z (5 months ago)
- Last Synced: 2025-05-18T01:28:47.161Z (5 months ago)
- Language: Python
- Size: 52.7 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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.
```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.