Kiro Power для источников знаний AWS, которые не индексирует официальный AWS Knowledge MCP Server.
Официальный сервер бесплатен, не требует аутентификации, поддерживается AWS и уже охватывает документацию AWS, блоги AWS, What's New, Well-Architected и статьи re:Post Knowledge Center. Перестраивать всё это означало бы собирать данные с недокументированных эндпоинтов ради контента, который уже доступен через поддерживаемый интерфейс.
Поэтому этот Power покрывает только то, что официальный сервер не обрабатывает. Границу определяет allow-list read_documentation:
Источник | Официальный сервер | Этот Power |
| Покрывается | — |
| Покрывается | — |
| Покрывается | — |
| Не покрывается | Полный поиск + чтение всей ветки |
| Не покрывается | Полный поиск по индексу + чтение в markdown |
| Не покрывается | 30 последних постов, только сводка |
Related MCP server: NexusNote MCP Server
uv в PATH (даёт uvx). Power не требует собственного Python: uv сам загрузит интерпретатор.
Официальный AWS Knowledge MCP Server — для всего, что в строках «Покрывается» выше. Этот Power намеренно не указывает его в своих зависимостях: клиенты, у которых он уже есть, не будут открывать второе подключение к тому же эндпоинту и загружать дублирующий набор инструментов. Если у вас его ещё нет, добавьте в ~/.kiro/settings/mcp.json:
{
"mcpServers": {
"aws-knowledge": {
"url": "https://knowledge-mcp.global.api.aws"
}
}
}Из GitHub (рекомендуется для команд)
Kiro → панель Powers → Add Custom Power → Import power from GitHub
Вставьте URL этого репозитория
Из локального клона
git clone https://github.com/owner/my-project
Kiro → панель Powers → Add Custom Power → Import power from a folder
Выберите склонированную папку
Без панели Powers — добавьте сервер напрямую в ~/.kiro/settings/mcp.json:
{
"mcpServers": {
"aws-knowledge-plus": {
"command": "uvx",
"args": [
"--from",
"git+https://github.com/hey-iam-ryan/kiro-power-aws-knowledge-plus@v0.1.0",
"aws-knowledge-plus-mcp"
],
"autoApprove": ["search_aws_knowledge", "read_aws_content", "list_sources"]
}
}
}Первый запуск разрешает дерево зависимостей и занимает несколько секунд; последующие запуски кэшируются uv.
Этот Power доступен только для чтения всем, кроме мейнтейнера. В публичном GitHub-репозитории могут делать push только те аккаунты, которые явно добавлены в коллабораторы, а таких нет. Клонирование, установка и создание форков не дают возможности изменить то, что устанавливают ваши коллеги.
Установки привязаны к тегу релиза, а не к main. Ссылка в mcp.json указывает на @v0.1.0, поэтому каждый участник команды запускает один и тот же проверенный код, даже если main уже ушёл вперёд. Никто не получает обновления молча.
Нужны изменения? Создайте issue или pull request. Мейнтейнер проверяет его, вливает и срезает новый тег; участники получат версию после обновления привязки.
Выпуск новой версии, для мейнтейнера:
# after merging changes to main
uv run python smoke_test.py # must pass: it hits the live sources
git tag v0.2.0 && git push origin v0.2.0
# then bump the @v0.1.0 pin in mcp.json and README.md, commit, and pushИнструмент | Назначение |
| Ищет по всем источникам параллельно. |
| Читает страницу как markdown, маршрутизируясь по хосту, и поддерживает явные маркеры обрыва для продолжения чтения. |
| Что покрыто, что нет и почему. |
Фрагменты поиска намеренно короткие: они нужны, чтобы выбрать, что читать, а не чтобы отвечать на вопрос.
re:Post — восстанавливается из структурированных данных schema.org QAPage ветки: вы получаете вопрос и все ответы с авторами, датами, голосами и пометкой принятого ответа. Без HTML-скрапинга, а блоки кода сохраняются целиком.
docs.kiro.dev — страница запрашивается со своей опубликованной markdown-версии (/docs/steering читает /docs/steering.md), поэтому снимать навигацию с сайта не приходится.
Builder Center — только сохранная из Atom-фида, с явным примечанием, что текст статьи не извлекался.
Переменная | Значение по умолчанию | Назначение |
|
| Установите |
|
| Время жизни кэша ответов, секунды. |
|
| Таймаут одного запроса, секунды. |
| Chrome-like UA | re:Post отбрасывает запросы не из браузера. Изменяйте осторожно. |
Builder Center структурно ограничен. Его единственный публичный индекс — Atom-лента на 30 записей, у которой ссылка next зациклена сама на себя, поэтому более старые записи не находятся. Страницы статей рендерятся на клиенте и не запрашиаяются. Поэтому этот источник имеет низкий вес и его можно отключить.
Глубина поиска на re:Post. Сайт учитывает pageSize, но не page, поэтому результаты приходят с одной увеличенной страницы, а не через глубокую пагинацию.
Недокументированные эндпоинты. Выдача поиска re:Post берётся из встроенного в страницу payload __NEXT_DATA__. Это не публичный API, и он может измениться. Адаптер поддерживает оба варианта payload, которые сайт сейчас отдаёт отдельных и выдаёт пустой результат вместо падения, если не находит ни одного, но редизайн сайта потребует доработки.
uv sync
uv run python smoke_test.py # live checks against all three sources
uv run aws-knowledge-plus-mcp # start on stdiosmoothing_test.py ходит на реальные сайты, поэтому работает и как индикатор: если источник изменит разметку или payload, он первым упадёт здесь.
Исходящие запросы ограничены списком разрешённых хостов и префиксов путей — аallow-list строится из объявленных в каждом адаптере хостов и префиксов. Редиректы обрабатываются по одному переходу за раз, и каждый переход испенняется заново, поэтому редирект не может увести загрузчик на хост, которого нет в списке. Принимается только https. Запросы не содержат учётных данных, а все источники публичные.
Apache-2.0
List the sources this server covers, their limits, and what it deliberately omits.
Call this when unsure whether a lookup belongs here or with the official AWS Knowledge MCP Server.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the tool's scope and even its deliberate omissions, but it does not explicitly state side-effect-freedom or other behavioral details; the 'List' framing implies a read operation without being emphatic.
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?
Two concise sentences front-load the purpose and immediately give the usage context. All every sentence earns its place; there is no 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?
The tool has no arguments and an output schema exists, so the description only needs to say what the tool is for and when to call it, which it does completely.
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, so there are no parameter ambiguities for the description to resolve.
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 states a specific verb and resource: 'List the sources this server covers, their limits, and what it deliberately omits.' This clearly distinguishes the tool from the search/read sibling tools.
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?
Explicitly says 'Call this when unsure whether a lookup belongs here or with the official AWS Knowledge MCP Server,' giving a direct when-to-use condition and pointing to the appropriate alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch a page from a supported source and return it as markdown.
Dispatches on the URL's host:
repost.aws/questions/<id> — the question plus every answer, rebuilt from
the page's schema.org QAPage data with authors, dates and vote counts.
kiro.dev/docs/... — the published markdown twin of the page, so the text
arrives without site chrome. Blog and changelog pages are converted from HTML.
builder.aws.com/content/... — title, plus a summary when the site's feed
published one. The body is client-rendered and is not retrieved, and the
response says so; hand the user the URL rather than paraphrasing what came
back. A title derived from the URL slug is flagged in meta.title_source.
Long pages are truncated with an explicit marker telling you the
start_index to resume from.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL returned by search_aws_knowledge. | |
| max_length | No | Maximum characters to return. | |
| start_index | No | Character offset, for continuing a long read. |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full behavioral disclosure burden. It explicitly discloses client-rendered builder.aws.com bodies are not retrieved, that titles from URL slugs are flagged in meta.title_source, that repost.aws content is rebuilt from schema.org, that kiro.dev uses a markdown twin, and that long pages are truncated with an explicit resume marker. This is unusually transparent.
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 a single-sentence summary, then uses a compact bulleted dispatch list for the distinct URL types, and closes with the important truncation behavior. Every sentence contributes unique information, and there is no filler or 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?
Given that an output schema already exists and the three parameters are fully described in the schema, the description does not need to restate return-value details or parameter defaults. It covers everything else an agent must know: which hosts are supported, what to expect from each, the client-rendered caveat, and the pagination/resumption mechanism.
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 adds useful behavioral context beyond the schema, particularly that long pages contain an explicit marker telling the agent which start_index to use next, and that the URL's host determines the extraction behavior. This is meaningful but not a large amount of added parameter detail.
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 opening sentence states a specific action and object: 'Fetch a page from a supported source and return it as markdown.' The host-based bullets further define exactly what each URL type yields, and the tool is clearly distinct from its siblings (search_aws_knowledge and list_sources), making it easy for an agent to know this is the content-fetching tool.
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 gives practical, source-specific usage guidance, such as not paraphrasing builder.aws.com content and resuming long reads using the returned start_index. It does not explicitly contrast this tool with its siblings, though the schema states the URL comes from search_aws_knowledge, so the intended workflow is clear but slightly implicit in the description itself.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search AWS knowledge sources that the official AWS Knowledge MCP Server does not cover.
Use this for: troubleshooting threads where someone hit the same error ('repost'), anything about Kiro itself such as steering, hooks, specs, powers or MCP configuration ('kiro'), and the AWS Builders' Library or other community write-ups ('builder').
The Builders' Library is worth calling out: aws.amazon.com/builders-library/
redirects to Builder Center, and the official server refuses the redirect
target, so this is the only way to find those articles here. Note that Builder
Center results carry no article body — see read_aws_content.
Do NOT use this for AWS service documentation, API references, AWS blogs, What's New announcements, Well-Architected guidance or re:Post Knowledge Center articles — the official AWS Knowledge MCP Server indexes those and is the better answer for them.
Sources are queried in parallel; one slow or broken source degrades to an
entry in errors rather than failing the call. Snippets are kept short on
purpose: pick the promising hits and pass their URLs to read_aws_content.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum hits to return. | |
| query | Yes | What to look for. Plain keywords work best. | |
| sources | No | Restrict the search to these sources: 'repost' (AWS re:Post community Q&A threads), 'kiro' (kiro.dev docs, blog, changelog), 'builder' (AWS Builder Center, including the AWS Builders' Library). Omit to search all of them. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full disclosure burden, and it delivers: parallel source querying, degraded single-source errors becoming entries in `errors` rather than failing the call, intentionally short snippets, the Builders' Library redirect quirk, and the caveat that Builder Center results carry no article body. These are exactly the behaviors an agent needs to calibrate expectations.
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 each paragraph earns its place: purpose, in-scope uses, exclusions, a redirect quirk callout, fail-degrade behavior, and a follow-up routing to read_aws_content. It is front-loaded with the purpose statement and organized in clear sections. There is minor redundancy (the Builders' Library is mentioned in the in-scope list and again in the callout), but the callout adds vital context, so the verbosity is largely justified.
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 is a search tool with only 3 parameters and an output schema, and the description covers everything an agent needs: what to search, when to search, when not to search, what each source maps to, failure behavior, and what to do with the results (pass URLs to read_aws_content). Combined with schema + output schema coverage, nothing material is missing.
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 coverage is 100%, so the baseline is 3. The description adds real value: it maps source values to intent ('repost' → troubleshooting threads; 'kiro' → steering/hooks/specs/MCP config; 'builder' → Builders' Library and community write-ups), explains why `builder` is non‑obvious in the redirect paragraph, and gives advice on how to proceed from the (short) snippets. It doesn't add much for `limit`, but the source semantics are significantly enriched.
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 opening sentence explains the specific action and scope: 'Search AWS knowledge sources that the official AWS Knowledge MCP Server does not cover.' It then enumerates the concrete resource categories ('repost', 'kiro', 'builder'), and contrasts itself with both the official server and its sibling read_aws_content, leaving no ambiguity about what this tool is and is not.
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 is exemplary: it gives a 'Use this for:' list with concrete examples (error threads, Kiro config, Builders' Library), then a 'Do NOT use this for:' list (service docs, API references, AWS blogs, What's New, etc.) with the explicit alternative recommendation: 'the official AWS Knowledge MCP Server ... is the better answer for them.' It also routes follow-on behavior to read_aws_content. Nothing is left to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.2.1search_aws_knowledge1 field changedInput schema / properties / sources / descriptionPrevious value: -"Restrict the search to these sources: 'repost' (AWS re:Post community Q&A), 'kiro' (kiro.dev docs, blog, changelog), 'builder' (recent AWS Builder Center posts). Omit to search all of them."New value: +"Restrict the search to these sources: 'repost' (AWS re:Post community Q&A threads), 'kiro' (kiro.dev docs, blog, changelog), 'builder' (AWS Builder Center, including the AWS Builders' Library). Omit to search all of them."v0.1.0list_sourcesread_aws_contentsearch_aws_knowledgeScored across 3 tools
Each tool has a clearly distinct role: search for finding content, list_sources for orienting on scope, and read_aws_content for retrieving a specific page. The descriptions explicitly cross-reference each other to prevent confusion.
All three tool names follow a consistent verb_noun snake_case pattern: search_aws_knowledge, list_sources, read_aws_content. The naming style is uniform and predictable.
Three tools is well-scoped for a focused supplementary server that deliberately covers only what the official AWS Knowledge MCP Server misses. Each tool fills an essential role in the search-and-read workflow without bloat.
The server covers the full intended lifecycle: discover what sources exist (list_sources), search them (search_aws_knowledge), and retrieve specific content (read_aws_content). This is a complete minimal surface for a supplemental knowledge server.
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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