The AWS Knowledge MCP server is a fully managed remote Model Context Protocol server that provides real-time access to official AWS content in an LLM-compatible format. It offers structured access to AWS documentation, code samples, blog posts, What's New announcements, Well-Architected best practices, and regional availability information for AWS APIs and CloudFormation resources. Key capabilities include searching and reading documentation in markdown format, getting content recommendations, listing AWS regions, and checking regional availability for services and features.
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Average 4.6/5 across 5 of 5 tools scored. Lowest: 4/5.
Each tool has a distinct, well-defined purpose: regional availability, region listing, doc reading, skill retrieval, and doc search. No overlapping functionality.
All tools follow a consistent 'aws___verb_noun' pattern, making their actions and targets immediately clear.
Five tools cover the core needs for an AWS knowledge server: searching, reading, listing regions, checking availability, and retrieving skills. The count is well-scoped.
The toolset covers major knowledge retrieval operations. A potential minor gap is absence of a tool for AWS service-specific information beyond documentation, but the existing tools handle most queries effectively.
aws___get_regional_availabilityAAWS resource availability per region.
Max 10 regions; multi-region needs filters; single-region supports next_token.
Status: isAvailableIn | isNotAvailableIn | isPlannedIn | Not Found.
Response key: products | service_apis | cfn_resources.
Not for region counts/docs/vague queries -- use search_documentation / list_regions.
Filter values must EXACTLY match AWS's catalog names; guessed, partial, or pluralized names are rejected ("values in filter parameter do not exist"). If unsure of the exact name, first call once for a single region with resource_type set and NO filters to list all valid names, then re-call filtering on the exact match.
| Name | Required | Description | Default |
|---|---|---|---|
| region | No | Unused; use `regions`. | |
| filters | No | Use exact AWS product or sub-feature name. - product: 'Amazon Bedrock' (service), or sub-features like 'Comprehend Auto Scaling', 'Latency-Based Routing', 'PrivateLink Support'. When the user names a specific sub-feature, filter on the sub-feature -- do NOT generalize to the parent service ('Amazon Comprehend'); that returns availability for the wrong scope. - api: 'SdkServiceId+Operation' (e.g. 'CloudFormation+CreateStack', 'IAM+GetSSHPublicKey') or 'SdkServiceId' (e.g. 'EC2'). Use a literal '+' between service and operation -- not space, colon, or hyphen. - cfn: 'AWS::EC2::Instance', 'AWS::Lambda::Function'. Include every region the user named; don't add filters they didn't request. Values must EXACTLY match AWS's catalog (e.g. 'AWS Lambda', not 'Lambda' or 'AWS Lambda Service'). If unsure of the exact name, first call once for one region with NO filters to list valid names, then filter on the exact match. | |
| regions | No | AWS region codes (max 10). Multi-region requires `filters`; single-region supports `next_token`. | |
| next_token | No | Pagination token. Single-region, no filters only. | |
| resource_type | Yes | Required: 'product' | 'api' | 'cfn'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Adds rich behavioral context beyond annotations: exact-match filter requirements with error example, pagination via `next_token` (single-region/no filters), status values, and response keys. No contradiction with readOnlyHint.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Bulleted, front-loaded structure with no fluff. Each sentence adds operational value—constraints, status, response keys, exclusions, and filter exact-match advice—all in a compact format.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no output schema, the description covers critical aspects: valid statuses, response key names, pagination constraints, filter matching pitfalls, and a recovery strategy. Sufficiently complete for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema already provides 100% parameter coverage, so baseline is 3. Description adds a concrete workflow for discovering exact filter names and warns against pluralized/partial values, which is beyond the schema. Slight extra value justifies 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States it provides 'AWS resource availability per region' with specific statuses (isAvailableIn, isNotAvailableIn, etc.) and response keys. Explicitly distinguishes from siblings: 'Not for region counts/docs/vague queries -- use `search_documentation` / `list_regions`.'
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Clearly states when not to use ('region counts/docs/vague queries') and names alternatives (`search_documentation`, `list_regions`). Also gives operational prerequisites: max 10 regions, multi-region requires `filters`, single-region supports `next_token`.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
aws___list_regionsARetrieve a list of all AWS regions.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows this is a safe read operation. The description adds no further behavioral context such as potential pagination, latency, or permission requirements, but the tool is simple enough that annotations may suffice.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, direct sentence with no wasted words or redundant information. It is front-loaded and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no parameters and no output schema, the description sufficiently explains both the action (retrieve) and the expected result (a list of all AWS regions). This is complete for the tool's simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the schema covers this completely. The description adds no parameter-specific detail because there are none, but the baseline score of 4 applies for tools with no parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific verb and resource: 'Retrieve a list of all AWS regions.' This is distinct from sibling tools like get_regional_availability, which focuses on availability rather than a plain list of regions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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. There are no exclusions, prerequisites, or mention of sibling tools that perform related but different operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
aws___read_documentationAFetch 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/.
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}.
| Name | Required | Description | Default |
|---|---|---|---|
| requests | No | List of `{url, max_length?, start_index?}`. Batch 2-5. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite the annotations already indicating read-only and non-destructive behavior, the description adds substantial behavioral context: it requires exact URLs from search (no guessing slugs), explains the allow-listed domain prefixes, details output fields including `truncated` and `redirected_url`, describes pagination via `start_index`/`end_index`, and lists possible error codes. This goes far beyond what annotations provide, giving the agent a full picture of the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is long but highly structured and front-loaded. The core purpose appears in the first sentence, followed by clearly formatted usage guidelines, parameter explanations, allow-list, and output/error details. Each section earns its place: the 'when to use' rules are crucial, the allow-list is essential for security, and the output spec is necessary given no output schema. No wasteful repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This tool has moderate complexity (nested request objects, batching, allow-listed URLs, pagination, error handling) and no output schema. The description covers all aspects: purpose, usage rules, parameter defaults, batching requirements, URL restrictions, output fields (`total_length`, `start_index`, `end_index`, `truncated`, `redirected_url`), and error codes. An agent has everything needed to select and operate the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already covers 100% of parameters with descriptions, providing a baseline of 3. The tool description adds meaningful semantics: defaults for `max_length` (10000) and `start_index` (0), how to use `end_index` to continue reading, and that `start_index` can be used to jump via TOC offset. This enhances the raw schema without being redundant, so a score above baseline is warranted.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource: 'Fetch full AWS doc pages as markdown.' It clearly distinguishes this tool from its sibling `search_documentation`, which returns verbatim page chunks, by emphasizing that this tool retrieves the complete page. The purpose is unambiguous and not a tautology.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool vs. `search_documentation`: avoid re-reading a URL whose chunk you already have, and only read the full page when chunks lack content (with specific examples like enumeration or off-topic search results). It also names the alternative tool and explains how to obtain exact URLs, making the usage conditions very clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
aws___retrieve_skillARetrieve an AWS skill (workflows, references). Returns SKILL.md, or file if given.
Call search_documentation FIRST and copy skill_name verbatim -- it is an opaque registry ID. Never guess or fabricate skill_name or file.
| Name | Required | Description | Default |
|---|---|---|---|
| file | No | Optional file path within the skill, copied as cited (e.g. `references/architecture.md`). Don't add or strip a `references/` prefix. Omit for SKILL.md. | |
| skill_name | Yes | Required. Exact `skill_name` from a search_documentation result, copied verbatim. Do not invent or modify. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare this as read-only and non-destructive. The description adds behavioral context by explaining that `skill_name` is an 'opaque registry ID' that must be copied verbatim, and warns against fabrication. It also discloses the return behavior (SKILL.md or specific file). This goes beyond the structured annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no wasted words. It front-loads the purpose and then provides a critical usage instruction. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (2 params, no output schema), the description is complete: it explains what is returned, the prerequisite step (search_documentation), and the need for exact input. The annotations cover safety aspects, so no additional behavioral disclosures are required.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description reinforces the need to copy `skill_name` verbatim and avoid guessing, but the schema already contains these details. The description adds minimal parameter-specific meaning beyond what the schema provides, so it stays at baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Retrieve an AWS skill (workflows, references). Returns SKILL.md, or `file` if given.' It specifies the resource (AWS skill) and the output behavior, and distinguishes itself from siblings by referencing the search_documentation workflow and the skill_name registry ID.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit when-to-use guidance: 'Call `search_documentation` FIRST and copy `skill_name` verbatim.' It also warns against guessing inputs. However, it does not explicitly mention alternatives or when not to use this tool, so it stops short of a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
aws___search_documentationAAWS docs search. Each result's context is verbatim page text -- a real chunk of the actual page, not a short snippet -- and usually already contains the answer, so answer directly from it. Use read_documentation only when the chunks genuinely lack the needed detail.
Pick ONE topic. Add a 2nd ONLY if query genuinely spans domains. Extra topics dilute ranking.
reference_documentation -- API/SDK/CLI specs, config params
current_awareness -- new/released/announced
troubleshooting -- errors, "how to fix" (NOT for conceptual/feature questions)
amplify_docs -- Amplify (+ language)
cdk_docs -- CDK concepts/guides
cdk_constructs -- CDK code samples, L3
cloudformation -- CFN/SAM templates
strands_docs -- Strands Agents SDK (its Skills/agents concepts go here, NOT agent_skills)
agent_skills -- this tool's guided skills (load via retrieve_skill)
general (default) -- architecture, best practices, tutorials, feature behavior
Results: rank_order (lower=better), url, title, context (verbatim page chunk -- answer directly from it).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of results to return (default 4). | |
| topics | No | Up to 3 from: reference_documentation, current_awareness, troubleshooting, amplify_docs, cdk_docs, cdk_constructs, cloudformation, agent_skills, strands_docs, general. Default ["general"]. | |
| search_phrase | Yes | Keywords; preserve exact error strings and all task terms verbatim. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnlyHint=true, destructiveHint=false), the description discloses key behavioral traits: each result's context is 'verbatim page text' that 'usually already contains the answer', and that 'Extra topics dilute ranking.' These are non-obvious behavioral details that help the agent interpret results correctly, with no contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with purpose and key behavior, then uses a well-organized bullet list for topics. Every sentence earns its place: the topic list is necessary, and the result format note is essential in the absence of an output schema. No fluff or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 3 parameters, no output schema, and moderate complexity, the description provides full context: it explains result fields (rank_order, url, title, context), the nature of context (verbatim chunks), and how to bridge to read_documentation. It covers all needed operational knowledge for an agent to select and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Although the schema has 100% parameter descriptions, the description adds significant meaning by defining each topic category (e.g., 'troubleshooting -- errors, "how to fix" (NOT for conceptual/feature questions)') and explaining topic selection strategy. This provides semantic enrichment beyond the schema's terse 'topic name' descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description begins with 'AWS docs search', clearly stating the tool's verb (search) and resource (AWS docs). It distinguishes itself from siblings by explicitly directing users to 'read_documentation' only when chunks lack detail, and references 'retrieve_skill' in the topic list. This is a specific, differentiated purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit when-to-use guidance: 'Use read_documentation only when the chunks genuinely lack the needed detail,' and instructs 'Pick ONE topic. Add a 2nd ONLY if query genuinely spans domains.' It also clarifies that troubleshooting is NOT for conceptual/feature questions. This fully covers usage context and alternatives.
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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{
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