Amazon Bedrock Knowledge Bases Retrieval MCP Server

Category: AI Integration – MCP Servers
Brand: Amazon Web Services
Website: https://skywork.ai/skypage/en/jean-ibarz-mcp-server-deep-dive/1978309469924151296
Source Code / Project Type: Open‑source MCP server

Overview

The Amazon Bedrock Knowledge Bases Retrieval MCP Server is an open‑source Model Context Protocol (MCP) server that connects AI agents (such as Claude or other MCP‑compatible clients) to Amazon Bedrock Knowledge Bases. It enables retrieval‑augmented generation (RAG) over enterprise or private knowledge, allowing models to answer questions grounded in your own data with citation support.

Key Capabilities

• Exposes Amazon Bedrock Knowledge Bases to AI agents via MCP tools
• Supports retrieval‑augmented generation workflows using stored enterprise or private content
• Returns cited knowledge snippets so models can attribute answers to specific sources
• Designed to work with desktop MCP clients (e.g., Claude Desktop) and other MCP‑compatible tools

Features

• MCP-based integration

  • Implements the Model Context Protocol to act as a standardized bridge between LLMs and Amazon Bedrock Knowledge Bases.
  • Allows AI agents to query external knowledge without custom, one‑off integrations.

• Knowledge base retrieval for RAG

  • Provides tools that retrieve relevant documents or passages from Amazon Bedrock Knowledge Bases.
  • Supports retrieval‑augmented generation so models can ground responses in factual, organization‑specific data.

• Citation / source support

  • Returns retrieval results with associated metadata so answers can reference and cite underlying documents.

• Enterprise / private data access

  • Targets use cases like internal codebases, wikis, research collections, and other private datasets stored as Bedrock Knowledge Bases.

• Tooling for AI clients

  • Designed to be used from MCP‑compatible clients (e.g., Claude Desktop) as a set of tools.
  • Includes at least a retrieve_knowledge tool with parameters for querying the knowledge base (exact parameter list not fully available in the provided content).

• Configurable retrieval behavior

  • Behavior can be configured via MCP server settings and/or tool parameters (e.g., query text; likely additional options such as max results or filters, though the exact set isn’t fully visible in the excerpt).

• Embeddings provider flexibility (contextual)

  • The article discusses choosing an embedding provider such as Ollama or HuggingFace as part of a broader “Knowledge Base MCP Server” architecture. While Amazon Bedrock Knowledge Bases handle their own embeddings internally, the guide situates this server within RAG architectures that may use different embedding backends for other components.

• Architecture transparency

  • The source article covers core components and architectural breakdown, helping users understand how retrieval, querying, and MCP tools interact (details not fully visible in the excerpt but implied by sections like “Core Components Breakdown” and “Key Features at a Glance”).

• Installation and integration guidance

  • Step‑by‑step instructions (in the original article) for installing and running the MCP server.
  • Walkthroughs for integrating it with Claude Desktop and using the exposed tools.
  • Troubleshooting section for common installation and usage issues.

• Usage documentation

  • Example workflows demonstrating how to:
    • Connect Claude (or another MCP client) to the server
    • Issue knowledge retrieval queries
    • Use the returned, cited snippets in conversations or automation.

• Real‑world use cases (conceptual)

  • Suggested scenarios like personal assistants, team knowledge hubs, and other RAG‑driven applications that rely on Bedrock‑hosted knowledge.

Installation & Integration

High‑level (details are in the source guide):

• Install and configure the Knowledge Base Retrieval MCP Server from its open‑source repository.
• Register the server as an MCP tool provider in your MCP‑compatible client (e.g., Claude Desktop configuration file).
• Use the provided tool(s), such as retrieve_knowledge, from within the AI client to query Amazon Bedrock Knowledge Bases.

Pricing

• The MCP server itself is described as open‑source; no specific paid plans are mentioned in the provided content.
• Any costs related to usage of Amazon Bedrock Knowledge Bases (e.g., storage, retrieval, model inference) are governed by AWS pricing and are not detailed in the excerpt.

Tags

• RAG
• Knowledge Base
• AWS