The AWS Well-Architected Framework MCP Server is a project that provides an MCP (Model Context Protocol) server offering guidance based on the six pillars of the AWS Well-Architected Framework: Operational Excellence, Security, Reliability, Performance Efficiency, Cost Optimization, and Sustainability. It allows users to interact with these principles programmatically via specific endpoints.
This server helps developers and architects evaluate their architecture against AWS best practices by providing recommendations based on the Well-Architected Framework. It simplifies access to detailed pillar information and offers tools like architecture analysis and review templates to ensure cloud environments are optimized for performance, security, reliability, and cost-efficiency.
This tool is designed for cloud architects, developers, DevOps engineers, and IT professionals who want to design, evaluate, or optimize their AWS-based architectures using the Well-Architected Framework. It’s also useful for teams adopting AWS services and seeking automated guidance on architectural improvements.
The server can be deployed locally or in any environment that supports Python 3.10 or higher. Once set up, it runs as an HTTP service listening on port 3001 by default, making it accessible via API calls from various platforms.
You should use this server during the planning, development, and review phases of your AWS architecture lifecycle. It’s particularly helpful when designing new systems, optimizing existing ones, or conducting periodic reviews to align with AWS best practices.
To install, clone the repository using `git clone https://github.com/aws-samples/aws-well-architected-mcp.git`, create a virtual environment with Python 3.10+, activate it, and install dependencies using `pip install -e .`. Run the server with the command `mcp dev src/well_architected/server.py`.
Available endpoints include: `pillars://list` to list all framework pillars, `pillar://{name}` to get details about a specific pillar, `analyze_architecture` to analyze an architecture description, and `review_architecture` to gather architecture information through a template.
Yes, you can use `curl` commands to interact with the server. For example, use `curl http://localhost:3001/resource/pillars://list` to list all pillars or `curl -X POST` with JSON data to analyze an architecture.
The project includes a `pyproject.toml` file for configuration, and its source code resides under the `src/well_architected/` directory. Key files include `server.py` for the main logic and `data/pillars.py` for pillar-related data.
MCP (Model Context Protocol) is an open protocol designed to standardize how applications provide context information to large language models (LLMs). Like a 'USB-C port' for AI applications, MCP ensures AI models can seamlessly connect with various data sources and tools.
An MCP Server is a server that supports the MCP protocol, enabling the exchange of contextual information between applications and AI models in a standardized way. It provides developers with an easy way to integrate AI models with databases, APIs, or other data sources.
An MCP Server eliminates the complexity of developing custom adapters by unifying the connection between AI models and various data sources. Whether you're a developer, data scientist, or AI app builder, an MCP Server simplifies the integration process, saving time and resources.
An MCP Server acts as an intermediary bridge, converting contextual information from various data sources into a format that AI models can understand. By adhering to the MCP protocol, it ensures data is transmitted between applications and AI models in a standardized manner.
At mcpserver.shop, you can browse our MCP Server Directory. The directory is categorized by industry (e.g., finance, healthcare, education), and each server comes with detailed descriptions and tags to help you quickly find the option that suits your needs.
The MCP Server Directory on mcpserver.shop is free to browse. However, some servers are hosted by third-party providers and may involve usage fees. Check the detailed page of each server for specific information.
MCP Servers support a wide range of data sources, including databases, APIs, cloud services, and custom tools. The flexibility of the MCP protocol allows it to connect almost any type of data source to AI models.
MCP Servers are primarily designed for developers, data scientists, and AI app builders. However, mcpserver.shop provides detailed documentation and guides to help users of varying technical levels get started easily.
Yes, MCP is an open-source protocol that encourages community participation and collaboration. For more details or to contribute, visit the official MCP documentation.
On mcpserver.shop, each MCP Server’s detailed page includes the provider’s contact information or a link. You can directly reach out to the provider for more details or technical support.