AWS Documentation MCP Server
# AWS Documentation MCP Server
Model Context Protocol (MCP) server for AWS Documentation
This MCP server provides tools to access AWS documentation, search for content, and get recommendations.
## Features
- **Read Documentation**: Fetch and convert AWS documentation pages to markdown format
- **Search Documentation**: Search AWS documentation using the official search API (global only)
- **Recommendations**: Get content recommendations for AWS documentation pages (global only)
- **Get Available Services List**: Get a list of available AWS services in China regions (China only)
## Prerequisites
### Installation Requirements
1. Install `uv` from [Astral](https://docs.astral.sh/uv/getting-started/installation/) or the [GitHub README](https://github.com/astral-sh/uv#installation)
2. Install Python 3.10 or newer using `uv python install 3.10` (or a more recent version)
## Installation
| Cursor | VS Code |
|:------:|:-------:|
| [](https://cursor.com/install-mcp?name=awslabs.aws-documentation-mcp-server&config=eyJjb21tYW5kIjoidXZ4IGF3c2xhYnMuYXdzLWRvY3VtZW50YXRpb24tbWNwLXNlcnZlckBsYXRlc3QiLCJlbnYiOnsiRkFTVE1DUF9MT0dfTEVWRUwiOiJFUlJPUiIsIkFXU19ET0NVTUVOVEFUSU9OX1BBUlRJVElPTiI6ImF3cyJ9LCJkaXNhYmxlZCI6ZmFsc2UsImF1dG9BcHByb3ZlIjpbXX0%3D) | [](https://insiders.vscode.dev/redirect/mcp/install?name=AWS%20Documentation%20MCP%20Server&config=%7B%22command%22%3A%22uvx%22%2C%22args%22%3A%5B%22awslabs.aws-documentation-mcp-server%40latest%22%5D%2C%22env%22%3A%7B%22FASTMCP_LOG_LEVEL%22%3A%22ERROR%22%2C%22AWS_DOCUMENTATION_PARTITION%22%3A%22aws%22%7D%2C%22disabled%22%3Afalse%2C%22autoApprove%22%3A%5B%5D%7D) |
Configure the MCP server in your MCP client configuration (e.g., for Amazon Q Developer CLI, edit `~/.aws/amazonq/mcp.json`):
```json
{
"mcpServers": {
"awslabs.aws-documentation-mcp-server": {
"command": "uvx",
"args": ["awslabs.aws-documentation-mcp-server@latest"],
"env": {
"FASTMCP_LOG_LEVEL": "ERROR",
"AWS_DOCUMENTATION_PARTITION": "aws"
},
"disabled": false,
"autoApprove": []
}
}
}
```
> **Note**: Set `AWS_DOCUMENTATION_PARTITION` to `aws-cn` to query AWS China documentation instead of global AWS documentation.
or docker after a successful `docker build -t mcp/aws-documentation .`:
```json
{
"mcpServers": {
"awslabs.aws-documentation-mcp-server": {
"command": "docker",
"args": [
"run",
"--rm",
"--interactive",
"--env",
"FASTMCP_LOG_LEVEL=ERROR",
"--env",
"AWS_DOCUMENTATION_PARTITION=aws",
"mcp/aws-documentation:latest"
],
"env": {},
"disabled": false,
"autoApprove": []
}
}
}
```
## Basic Usage
Example:
- "look up documentation on S3 bucket naming rule. cite your sources"
- "recommend content for page https://docs.aws.amazon.com/AmazonS3/latest/userguide/bucketnamingrules.html"

## Tools
### read_documentation
Fetches an AWS documentation page and converts it to markdown format.
```python
read_documentation(url: str) -> str
```
### search_documentation (global only)
Searches AWS documentation using the official AWS Documentation Search API.
```python
search_documentation(search_phrase: str, limit: int) -> list[dict]
```
### recommend (global only)
Gets content recommendations for an AWS documentation page.
```python
recommend(url: str) -> list[dict]
```
### get_available_services (China only)
Gets a list of available AWS services in China regions.
```python
get_available_services() -> str
```
TDQS
Scored across 3 tools
Each tool has a clearly distinct purpose: read_documentation fetches and converts content, recommend provides related page suggestions, and search_documentation performs keyword searches. There is no overlap in functionality, making it easy for an agent to select the appropriate tool based on the task.
All tool names follow a consistent verb_noun pattern (read_documentation, recommend, search_documentation) using snake_case. The naming is predictable and aligned with their functions, enhancing readability and usability.
With only 3 tools, the server feels thin for an AWS documentation domain, which could involve more operations like filtering, summarizing, or managing documentation history. While the tools cover core reading, searching, and recommending, the scope might benefit from additional utilities to handle complex documentation workflows.
The tool set covers essential documentation interactions: reading, searching, and discovering related content. However, there are minor gaps, such as no tools for summarizing documentation, tracking changes, or interacting with user-specific documentation notes, which could limit advanced agent workflows.