AWS Knowledge Base Retrieval MCP Server: Streamlining Information Extraction for AI Applications

Overview: What is AWS Knowledge Base Retrieval MCP Server?

The AWS Knowledge Base Retrieval MCP Server is an implementation that leverages the Model Context Protocol (MCP) to facilitate the retrieval of information from the AWS Knowledge Base using the Bedrock Agent Runtime. This server enables seamless integration with various AI applications, providing a robust platform for users to augment their queries through contextual data extracted directly from specific knowledge bases.

🔧 Core Features & MCP Capabilities

The core feature of this MCP server is RAG (Retrieval-Augmented Generation), which means it can retrieve context-rich information from the AWS Knowledge Base aligned with specific user queries. Additionally, it supports customizable result retrieval, allowing users to specify and get a pre-set number or dynamic quantity of results.

Data Flow through MCP Protocol

Below is an illustration of how this data flows through the Model Context Protocol:

graph TD
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server]
    C --> D[Data Source/Tool]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

MCP Client Compatibility

This MCP server is designed to be compatible with various MCP clients, including:

• Claude Desktop: Full support for retrieval operations.
• Continue: Full support, ensuring seamless integration and enhanced user experience.
• Cursor: Support limited to tools; integration prompts are not available.

⚙️ MCP Architecture & Protocol Implementation

The architecture of the AWS Knowledge Base Retrieval MCP Server is built around a plugin-based structure that integrates seamlessly with the Model Context Protocol. It utilizes Docker for containerization, ensuring robustness and portability across different environments.

Building the Server

Docker users can build the server using this command:

docker build -t mcp/aws-kb-retrieval -f src/aws-kb-retrieval-server/Dockerfile .

🚀 Getting Started with Installation

To start using the AWS Knowledge Base Retrieval MCP Server, you first need to configure your environment with the necessary AWS credentials. These include:

1. AWS Access Key ID
2. AWS Secret Access Key
3. AWS Region

Ensure that these keys have the appropriate permissions for Bedrock Agent Runtime operations.

Docker Configuration Example

Add this to your claude_desktop_config.json file if you are using Docker:

Docker

{
  "mcpServers": {
    "aws-kb-retrieval": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm", "-e", "AWS_ACCESS_KEY_ID", "-e", "AWS_SECRET_ACCESS_KEY", "-e", "AWS_REGION",
        "mcp/aws-kb-retrieval-server"
      ],
      "env": {
        "AWS_ACCESS_KEY_ID": "YOUR_ACCESS_KEY_HERE",
        "AWS_SECRET_ACCESS_KEY": "YOUR_SECRET_ACCESS_KEY_HERE",
        "AWS_REGION": "YOUR_AWS_REGION_HERE"
      }
    }
  }
}

Non-Docker Configuration Example

Alternatively, you can configure the server using npx:

{
  "mcpServers": {
    "aws-kb-retrieval": {
      "command": "npx",
      "args": [
        "-y", "@modelcontextprotocol/server-aws-kb-retrieval"
      ],
      "env": {
        "AWS_ACCESS_KEY_ID": "YOUR_ACCESS_KEY_HERE",
        "AWS_SECRET_ACCESS_KEY": "YOUR_SECRET_ACCESS_KEY_HERE",
        "AWS_REGION": "YOUR_AWS_REGION_HERE"
      }
    }
  }
}

💡 Key Use Cases in AI Workflows

This server shines in various AI workflows, particularly where timely and accurate context is crucial. For instance:

1. Technical Support: Retrieve documentation snippets to resolve user queries.
2. Training Assistant: Extract relevant information from the knowledge base for training purposes.

Technical Implementation Scenario

Suppose an engineer needs immediate access to AWS-related technical support documents. By sending a query through the MCP protocol, this server can retrieve detailed and relevant documentation seamlessly.

🔌 Integration with MCP Clients

The AWS Knowledge Base Retrieval MCP Server is designed to work with various MCP clients, ensuring compatibility across different AI applications:

• Claude Desktop: Direct retrieval of contextual data.
• Continue: Customizable result retrieval for enhanced query generation.
• Cursor: Tool-based integration without prompt support.

📊 Performance & Compatibility Matrix

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	✅	Full Support
Cursor	❌	✅	❌	Tools Only

🛠️ Advanced Configuration & Security

Example MCP Configuration Code Sample

{
  "mcpServers": {
    "aws-kb-retrieval": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-aws-kb-retrieval"
      ],
      "env": {
        "AWS_ACCESS_KEY_ID": "YOUR_ACCESS_KEY_HERE",
        "AWS_SECRET_ACCESS_KEY": "YOUR_SECRET_ACCESS_KEY_HERE",
        "AWS_REGION": "YOUR_AWS_REGION_HERE"
      }
    }
  }
}

Setting Up AWS Credentials

Ensure you have the necessary AWS credentials and permissions to run Bedrock Agent Runtime operations.

❓ Frequently Asked Questions (FAQ)

1. Does this server support multiple result retrieval?

   • Yes, it does with the option to specify n results.

2. Can I use this server without Docker or npx?

   • While Docker and npm are recommended for ease of setup, manual installation is possible if needed.

3. What permissions do AWS keys need for this server to function properly?

   • The keys must have access to Bedrock Agent Runtime operations.

4. Is there a limit to the number of results that can be retrieved at once?

   • There are no explicit limits, but practical limitations may apply due to network and computational constraints.

5. How do I troubleshoot connection issues with the AWS Knowledge Base through this server?

   • Ensure proper environment variables and Docker configurations are set correctly.

👨‍💻 Development & Contribution Guidelines

To contribute to the AWS Knowledge Base Retrieval MCP Server, follow these steps:

1. Clone the repository.
2. Install dependencies using npm install.
3. Contribute code or documentation as needed.
4. Submit a pull request detailing your changes and contributions.

🌐 MCP Ecosystem & Resources

For more information on MCP servers and clients, visit the official Model Context Protocol website and GitHub repositories.

Example of Real AI Workflow Use Case

1. Data-Driven Decision Making: A financial analyst uses this server to quickly retrieve relevant market documents for analysis before making an investment decision.
2. Technical Support Automation: A customer service representative queries technical issues for immediate resolution, enhancing the speed and accuracy of support.

By integrating the AWS Knowledge Base Retrieval MCP Server into your projects, you ensure that AI applications can leverage rich contextual data from the AWS Knowledge Base, thereby improving performance and user experience in various workflows.