AWS Documentation MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| read_documentationA | Fetch and convert an AWS documentation page to markdown format. UsageThis tool retrieves the content of an AWS documentation page and converts it to markdown format. For long documents, you can make multiple calls with different start_index values to retrieve the entire content in chunks. URL Requirements
Example URLs
Output FormatThe output is formatted as markdown text with:
Handling Long DocumentsIf the response indicates the document was truncated, you have several options:
Args: ctx: MCP context for logging and error handling url: URL of the AWS documentation page to read max_length: Maximum number of characters to return start_index: On return output starting at this character index Returns: Markdown content of the AWS documentation |
| search_documentationA | Search AWS documentation using the official AWS Documentation Search API. UsageThis tool searches across all AWS documentation for pages matching your search phrase. Use it to find relevant documentation when you don't have a specific URL. Search Tips
Result InterpretationEach SearchResponse includes:
Args: ctx: MCP context for logging and error handling search_phrase: Search phrase to use search_intent: The intent behind the search requested by the user limit: Maximum number of results to return product_types: Filter by AWS product/service guide_types: Filter by guide type Returns: List of search results with URLs, titles, query ID, context snippets, and facets for filtering |
| recommendA | Get content recommendations for an AWS documentation page. UsageThis tool provides recommendations for related AWS documentation pages based on a given URL. Use it to discover additional relevant content that might not appear in search results. Recommendation TypesThe recommendations include four categories:
When to Use
Finding New FeaturesTo find newly released information about a service:
Result InterpretationEach recommendation includes:
Args: ctx: MCP context for logging and error handling url: URL of the AWS documentation page to get recommendations for Returns: List of recommended pages with URLs, titles, and context |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
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.