Integrating AI Agents with Existing Backend Using MCP and Strands Agents SDK

Описание и применение

Внедрение агента ИИ в существующее приложение с использованием MCP и Strands Agents SDK позволяет выполнять чтение данных напрямую из MongoDB и безопасно изменять данные через контролируемый API, сохраняя бизнес‑логику.

Задача

Traditional web applications lack a standardized way to expose functionality to AI agents, leading to inefficient custom tool creation and potential data integrity risks.

Результат

The dual‑server MCP strategy enabled the AI agent to perform unlimited analytical queries on inventory data while ensuring all write operations adhered to existing business rules, eliminating the need for custom tool development and maintaining data integrity.

Рабочий процесс

• 1. Expose a JSON‑RPC MCP endpoint at '/mcp' on the SpringBoot backend.
• 2. Deploy a MongoDB MCP server to provide read‑only tools and resources to the agent.
• 3. Configure the Strands Agents SDK to add clients for both MCP servers.
• 4. Use the SDK’s context‑adding methods to restrict the agent’s access to authorized data (e.g., tenant info, user roles).
• 5. Connect the SDK to Amazon Bedrock (Claude Sonnet) by setting AWS credentials in the environment.
• 6. Deploy the agent, which now can read inventory data via the Database MCP and write updates via the Application MCP while respecting business logic.

Доказательства

The MCP server does not use AI. It is simply a JSON RPC endpoint served at '/mcp' allowing the agent to retrieve the following, Tools, Resources, Prompts.

Источник

I highly recommend a dual-server approach.

Источник

Connecting to Amazon Bedrock models require almost zero configuration.

Источник