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AWS Knowledge

by aws.api.global.knowledge-mcp

aws___read_documentation

Read-only

Fetch full AWS doc pages as markdown. search_documentation already returns verbatim page chunks, so don't re-read a URL whose chunk you already have to "confirm" or "round out" an answer -- the chunk is the real page text; treat it as authoritative.

Reading the full page is justified ONLY when the chunks genuinely lack the content:

  • an enumeration or aggregation ("list all X", "how many X") needs the complete set and the chunks show only part of it;

  • no search result is on-topic after refining the query, and a known doc URL would have the answer. Otherwise, answer from the chunks. Use exact URLs from search_documentation; don't guess slugs.

Input: requests: [{url, max_length?, start_index?}]. Batch 2-5.

  • max_length default 10000.

  • start_index default 0; use prior end_index to continue, TOC offset to jump.

Allow-listed prefixes: docs.aws.amazon.com; aws.amazon.com (not /marketplace); repost.aws/knowledge-center; docs.amplify.aws; ui.docs.amplify.aws; github.com/{aws-cloudformation/aws-cloudformation-templates, aws-samples/{aws-cdk-examples, generative-ai-cdk-constructs-samples, serverless-patterns}, awsdocs/aws-cdk-guide, awslabs/aws-solutions-constructs, cdklabs/cdk-nag} (README on main); constructs.dev/packages/{@aws-cdk-containers, @aws-cdk, @cdk-cloudformation, aws-analytics-reference-architecture, aws-cdk-lib, cdk-amazon-chime-resources, cdk-aws-lambda-powertools-layer, cdk-ecr-deployment, cdk-lambda-powertools-python-layer, cdk-serverless-clamscan, cdk8s, cdk8s-plus-33}; strandsagents.com/latest/documentation/docs/; karpenter.sh/docs/; Amazon Braket: {amazon-braket-sdk-python, amazon-braket-schemas-python, amazon-braket-default-simulator-python, amazon-braket-pennylane-plugin-python, amazon-braket-algorithm-library, qiskit-braket-provider, autoqasm, qirtoqasm}.readthedocs.io and github.com/amazon-braket/* (blob/tree/raw).

Output: SUCCESS -- markdown + total_length, start_index, end_index, truncated, redirected_url? (truncated includes TOC with char ranges). ERROR -- error_code in {not_found, invalid_url, throttled, downstream_error, validation_error}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
requestsNoList of `{url, max_length?, start_index?}`. Batch 2-5.

TDQS

A4.6/5.0
Behavior4/5

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

The annotations already declare readOnlyHint=true and destructiveHint=false. The description adds significant behavioral detail beyond that: it describes the output (markdown, total_length, start_index, end_index, truncated, redirected_url), error codes, and the behavior when truncated (includes TOC with char ranges). No contradictions.

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?

The description is long but well-structured: first sentence states the purpose, followed by clear usage rules, input/output details, allowed URL prefixes, and error outputs. It is front-loaded with the most critical information. The list of allowed prefixes is lengthy but necessary for correct invocation. Every sentence adds value.

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

Completeness5/5

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

Given there is no output schema, the description fully explains both success and error return formats. It covers input structure, batching, continuation mechanics, and explicit allowed URL prefixes. All aspects needed for correct invocation are addressed, leaving no critical gaps.

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?

Schema coverage is 100% and each parameter has a schema description. The tool description adds practical usage semantics: it specifies batching 2-5, default values for max_length (10000) and start_index (0), and hints for using prior end_index to continue or TOC offset to jump. This goes beyond the schema's structural documentation.

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 'Fetch full AWS doc pages as markdown', which is a specific verb-resource pair. It distinguishes itself from the sibling tool 'search_documentation' by explicitly noting that search returns chunks, while this tool returns the full page.

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

Usage Guidelines5/5

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

Exceptionally detailed guidelines are provided: it explicitly tells the agent when NOT to use this tool (when a chunk from search already contains the needed text) and when it IS justified (enumeration/aggregation or no relevant search result). It also instructs to use exact URLs from search and not to guess slugs, which prevents misuse.

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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TDQS

A4.4/5.0
Disambiguation5/5

Each tool targets a distinct aspect of AWS knowledge: regional availability, region listing, documentation search, full page retrieval, and skill retrieval. Descriptions clearly differentiate when to use each, especially distinguishing search from read_documentation.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern with snake_case, prefixed by 'aws___.' Names like get_regional_availability, list_regions, read_documentation, retrieve_skill, and search_documentation are descriptive and predictable.

Tool Count5/5

Five tools is well-scoped for a knowledge server. Each tool provides a core function (search, full doc retrieval, region listing, availability check, skills) without redundancy or excessive specialization.

Completeness4/5

The tool set covers primary AWS knowledge needs: searching documentation, reading full pages, listing regions, checking regional availability, and retrieving skills. Minor gap: no direct tool for enumerating all services, but search_documentation can handle that.

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