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Joseph19820124

AWS Documentation MCP Server

read_documentation

Fetch an AWS documentation page and convert it to markdown format. Supports chunking for long documents.

Instructions

Fetch and convert an AWS documentation page to markdown format.

## Usage

This 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

- Must be from the docs.aws.amazon.com domain
- Must end with .html

## Example URLs

- https://docs.aws.amazon.com/AmazonS3/latest/userguide/bucketnamingrules.html
- https://docs.aws.amazon.com/lambda/latest/dg/lambda-invocation.html

## Output Format

The output is formatted as markdown text with:
- Preserved headings and structure
- Code blocks for examples
- Lists and tables converted to markdown format

## Handling Long Documents

If the response indicates the document was truncated, you have several options:

1. **Continue Reading**: Make another call with start_index set to the end of the previous response
2. **Stop Early**: For very long documents (>30,000 characters), if you've already found the specific information needed, you can stop reading

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesURL of the AWS documentation page to read
max_lengthNoMaximum number of characters to return.
start_indexNoOn return output starting at this character index, useful if a previous fetch was truncated and more content is required.
Behavior3/5

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

No annotations provided, so description carries full burden. It covers conversion to markdown, chunking behavior, and URL constraints. However, lacks details on error handling, rate limits, or authentication.

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

Conciseness4/5

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

Well-structured with headings, bullet points, and examples. Every section serves a purpose without unnecessary fluff. Could be slightly shorter but remains clear.

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

Completeness4/5

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

Covers key aspects: URL requirements, output format, handling long documents. Missing some edge cases but sufficient for typical usage.

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

Parameters4/5

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

100% schema coverage provides baselines. Description adds value by explaining how to use start_index for continuation, which is beyond the schema's description.

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

Purpose5/5

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

The description clearly states the tool fetches and converts an AWS documentation page to markdown, with specific URL requirements and examples. It distinguishes from sibling tools (recommend, search_documentation) by focusing on retrieving a specific page.

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

Usage Guidelines4/5

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

Provides clear guidance on handling long documents with start_index and continuation strategies. Lacks explicit 'when not to use' but the context and sibling names imply appropriate use cases.

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

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