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Provides access to Amazon Business API docs, sample code, and troubleshooting resources for quick integration.
Configuration
View docs{
"mcpServers": {
"amazonbusiness-ab-integrations-mcp-server": {
"command": "npx",
"args": [
"@amazonbusiness/ab-integrations-mcp-server@latest",
"--dev-account-id=<account_id>"
]
}
}
}The Amazon Business Integrations MCP Server connects your development environment to Amazon Business API documentation, sample code, and troubleshooting resources. It helps you quickly search for information, read relevant documentation, and generate code samples to accelerate your API integration work.
Use this MCP server with an MCP client to search for API documentation using natural language queries and retrieve complete documentation content by document references. You can ask it questions like how to integrate Product Search API into a React shopping app, how to authenticate requests, or how to test different API calls. The server acts as a bridge between your IDE and the Amazon Business knowledge base, enabling fast access and guided code generation to speed up development.
Follow these concrete steps to install the MCP server in your preferred IDE environment.
{
"mcpServers": {
"ab-integrations-mcp-server": {
"command": "npx",
"args": ["@amazonbusiness/ab-integrations-mcp-server@latest", "--dev-account-id=<account_id>"],
"disabled": false,
"autoApprove": []
}
}
}Prerequisites include having an IDE that supports MCPs and developer agents, such as Amazon Q, and having Node.js with npm installed. You also need your account ID from the Solution Provider Portal to complete the MCP server configuration. If you only plan to search and read documentation, start with Step 1 to create your developer account and generate your account ID. If you also plan to generate code, complete Step 1, Step 2, and Step 3 to obtain access and refresh tokens for production API calls.
Authentication is not required for basic usage of the MCP server, but rate limits apply. For support, contact [email protected].
Performs semantic searches across the documentation corpus using natural language queries and returns relevant snippets with document references.
Retrieves complete documentation content using a document reference obtained from search results.