Which integrations are available for this server?

Supports deployment as a Docker container with options for using IAM Access Keys or AWS SSO authentication methods. Retrieves information from the AWS Knowledge Base using Bedrock Agent Runtime, supporting RAG (Retrieval-Augmented Generation) with customizable result counts and providing both raw context and structured metadata from search queries.

How do I use AWS Knowledge Base Retrieval MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@AWS Knowledge Base Retrieval MCP Server retrieve best practices for securing S3 buckets from knowledge base kb-aws-security" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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AWS Knowledge Base Retrieval MCP Server

by sammcj

Overview Schema Related Servers Score Discussions

JavaScript

Remote

AWS Knowledge Base Retrieval MCP Server

An MCP server implementation for retrieving information from the AWS Knowledge Base using the Bedrock Agent Runtime.

Features

RAG (Retrieval-Augmented Generation): Retrieve context from the AWS Knowledge Base based on a query and a Knowledge Base ID.
Supports multiple results retrieval: Option to retrieve a customizable number of results.

Related MCP server: DuckDB-RAG-MCP-Sample

Tools

retrieve_from_aws_kb
- Perform retrieval operations using the AWS Knowledge Base.
- Inputs:
  - query (string): The search query for retrieval.
  - knowledgeBaseId (string): The ID of the AWS Knowledge Base.
  - n (number, optional): Number of results to retrieve (default: 3).
- Response format:
  - The response now returns two separate content items:
    - A text item containing the raw context from the knowledge base.
    - A JSON item containing the structured RAG sources with metadata (id, fileName, snippet, and score).
  - This separation allows for more flexible processing of the results.

Configuration

Setting up AWS Credentials

You have two options for configuring AWS credentials:

Option 1: IAM Access Keys

Obtain AWS access key ID, secret access key, and region from the AWS Management Console.
Ensure these credentials have appropriate permissions for Bedrock Agent Runtime operations.
Set the environment variables as shown in the configuration examples below.
For temporary credentials, you can also provide a session token using the AWS_SESSION_TOKEN environment variable.

Option 2: AWS SSO (Single Sign-On)

The server now supports AWS SSO credentials:

Configure AWS CLI with your SSO profile: aws configure sso
Set only the AWS_REGION environment variable in the MCP server configuration.
The server will use the default credential provider chain, which includes SSO credentials.

Optional: Configure Default Knowledge Base IDs

You can optionally specify one or more knowledge base IDs to use by default:

Create an array of knowledge base IDs in JSON format.
Set this as the AWS_KB_IDS environment variable in your configuration.
When this is configured, the knowledgeBaseId parameter becomes optional in the tool.

Usage with Claude Desktop

Add this to your claude_desktop_config.json:

Docker with IAM Access Keys

{
  "mcpServers": {
    "aws-kb-retrieval": {
      "command": "docker",
      "args": [ "run", "-i", "--rm", "-e", "AWS_ACCESS_KEY_ID", "-e", "AWS_SECRET_ACCESS_KEY", "-e", "AWS_REGION", "-e", "AWS_KB_IDS", "mcp/aws-kb-retrieval-server" ],
      "env": {
        "AWS_ACCESS_KEY_ID": "YOUR_ACCESS_KEY_HERE",
        "AWS_SECRET_ACCESS_KEY": "YOUR_SECRET_ACCESS_KEY_HERE",
        "AWS_SESSION_TOKEN": "YOUR_OPTIONAL_SESSION_ID_FOR_SSO_TEMPORARY_CREDENTIALS_HERE",
        "AWS_REGION": "YOUR_AWS_REGION_HERE",
        "AWS_KB_IDS": "[\"kb-12345\", \"kb-67890\"]"
      }
    }
  }
}

Docker with AWS SSO

{
  "mcpServers": {
    "aws-kb-retrieval": {
      "command": "docker",
      "args": [ "run", "-i", "--rm", "-e", "AWS_REGION", "-e", "AWS_KB_IDS", "-v", "${HOME}/.aws:/root/.aws", "mcp/aws-kb-retrieval-server" ],
      "env": {
        "AWS_ACCESS_KEY_ID": "YOUR_ACCESS_KEY_HERE",
        "AWS_SECRET_ACCESS_KEY": "YOUR_SECRET_ACCESS_KEY_HERE",
        "AWS_SESSION_TOKEN": "YOUR_OPTIONAL_SESSION_ID_FOR_SSO_TEMPORARY_CREDENTIALS_HERE",
        "AWS_REGION": "YOUR_AWS_REGION_HERE",
        "AWS_KB_IDS": "[\"kb-12345\", \"kb-67890\"]"
      }
    }
  }
}

NPX with IAM Access Keys

{
  "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_SESSION_TOKEN": "YOUR_OPTIONAL_SESSION_ID_FOR_SSO_TEMPORARY_CREDENTIALS_HERE",
        "AWS_REGION": "YOUR_AWS_REGION_HERE",
        "AWS_KB_IDS": "[\"kb-12345\", \"kb-67890\"]"
      }
    }
  }
}

NPX with AWS SSO

{
  "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_SESSION_TOKEN": "YOUR_OPTIONAL_SESSION_ID_FOR_SSO_TEMPORARY_CREDENTIALS_HERE",
        "AWS_REGION": "YOUR_AWS_REGION_HERE",
        "AWS_KB_IDS": "[\"kb-12345\", \"kb-67890\"]"
      }
    }
  }
}

Local Repository (from cloned/built repo)

{
  "mcpServers": {
    "aws-kb": {
      "command": "node",
      "args": [
        "/path/to/mcp-aws-kb/dist/index.js"
      ],
      "env": {
        "AWS_ACCESS_KEY_ID": "YOUR_ACCESS_KEY_HERE",
        "AWS_SECRET_ACCESS_KEY": "YOUR_SECRET_ACCESS_KEY_HERE",
        "AWS_SESSION_TOKEN": "YOUR_OPTIONAL_SESSION_ID_FOR_SSO_TEMPORARY_CREDENTIALS_HERE",
        "AWS_REGION": "YOUR_AWS_REGION_HERE",
        "AWS_KB_IDS": "[\"kb-12345\", \"kb-67890\"]"
      },
      "disabled": false,
      "autoApprove": [
        "retrieve_from_aws_kb"
      ],
      "timeout": 120
    }
  }
}

Building

Docker:

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

License

This MCP server is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.

This README assumes that your server package is named @modelcontextprotocol/server-aws-kb-retrieval. Adjust the package name and installation details if they differ in your setup. Also, ensure that your server script is correctly built and that all dependencies are properly managed in your package.json.

Available Tools

1 tool

retrieve_from_aws_kbC

Performs retrieval from the AWS Knowledge Base using the provided query and Knowledge Base ID.

ParametersJSON Schema

Name	Required	Description
`query`	Yes	The query to perform retrieval on
`knowledgeBaseId`	Yes	The ID of the AWS Knowledge Base
`n`	No	Number of results to retrieve

TDQS

C2.9/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions retrieval but fails to describe key traits like authentication requirements, rate limits, error handling, or what the retrieval entails (e.g., returns text snippets, metadata). This leaves significant gaps in understanding how the tool behaves in practice.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that directly states the tool's function and inputs without any fluff. It's front-loaded with the core purpose and appropriately sized for the task, making it easy to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations and no output schema, the description is incomplete. It doesn't explain what the retrieval returns (e.g., format, content), error conditions, or operational details like latency or costs. For a tool with 3 parameters and missing structured data, this leaves the agent under-informed about critical context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents all parameters (query, knowledgeBaseId, n). The description adds no additional meaning beyond what's in the schema, such as examples or constraints. This meets the baseline for high schema coverage but doesn't enhance parameter understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('performs retrieval') and the resource ('from the AWS Knowledge Base'), making the purpose immediately understandable. It specifies the required inputs (query and Knowledge Base ID), which helps distinguish it from generic retrieval tools. However, without sibling tools, it doesn't need to differentiate further, so it's not a perfect 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives, prerequisites, or contextual constraints. It merely states what the tool does without indicating appropriate scenarios or limitations, leaving the agent without usage direction.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

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TDQS

B3.1/5.0

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined and distinct by default.

Naming Consistency5/5

A single tool inherently has perfect naming consistency, as there are no other tools to compare it against for patterns or conventions.

Tool Count2/5

One tool is too few for a server focused on AWS Knowledge Base retrieval, as it lacks essential operations like listing knowledge bases, managing queries, or handling configurations, making the scope feel incomplete and limiting.

Completeness2/5

The tool set is severely incomplete for the domain of AWS Knowledge Base operations. It only provides retrieval, missing critical functions such as creating, updating, or deleting knowledge base entries, which are necessary for a full workflow.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

Need Help?

Reddit Discussion

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AWS Knowledge Base Retrieval MCP Server

Features

Tools

Configuration

Setting up AWS Credentials

Option 1: IAM Access Keys

Option 2: AWS SSO (Single Sign-On)

Optional: Configure Default Knowledge Base IDs

Usage with Claude Desktop

Docker with IAM Access Keys

Docker with AWS SSO

NPX with IAM Access Keys

NPX with AWS SSO

Local Repository (from cloned/built repo)

Building

License

Available Tools

TDQS

TDQS

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

Ragie Model Context Protocol

DuckDB-RAG-MCP-Sample

RAG Information Retriever

LangChain Agent RAG MCP

Appeared in Searches

Latest Blog Posts

MCP directory API