模型上下文协议 (MCP) 服务器，提供跨多个运行时的 AWS Lambda Powertools 文档搜索功能。
按照安装说明操作，请遵循Claude Desktop 用户的模型上下文协议快速入门。您需要在 MCP 配置文件中添加如下部分：
{
"mcpServers": {
"powertools": {
"command": "npx",
"args": [
"-y",
"@serverless-dna/powertools-mcp"
]
}
}
}Related MCP server: MCP-Ragdocs
该项目实现了一个 MCP 服务器，使大型语言模型 (LLM) 能够搜索 AWS Lambda Powertools 文档。它使用 lunr.js 实现高效的本地搜索功能，并提供可汇总并呈现给用户的结果。
符合 MCP 标准的服务器，用于与 LLM 集成
使用 lunr.js 索引进行本地搜索
支持多种运行时：
Python
TypeScript
Java
。网
特定版本的文档搜索（默认为最新版本）
# Install dependencies
pnpm install
# Build the project
pnpm build该服务器可以作为通过 stdio 进行通信的 MCP 服务器运行：
npx -y @serverless-dna/powertools-mcp服务器提供了一个search_docs工具，其参数如下：
search ：搜索查询字符串
runtime ：要搜索的 Powertools 运行时（python、typescript、java、dotnet）
version ：可选版本字符串（默认为“最新”）
src/ ：源代码
index.ts ：主服务器实现
searchIndex.ts ：搜索索引管理
indexes/ ：为每个运行时预先构建的 lunr.js 搜索索引
dist/ ：编译输出
pnpm buildpnpm test在开发过程中，您可以使用以下配置通过 Claude Desktop 运行 MCP 服务器。
以下配置展示了如何在使用 Windows Subsystem for Linux (WSL) 进行开发时在 Windows Claude 桌面上运行。在 Mac 或 Linux 环境中，您可以以类似的方式运行。
输出是一个捆绑文件，由于所有依赖项都已捆绑，因此它使安装在 Windows 中的 Node 能够运行 MCP 服务器。
{
"mcpServers": {
"powertools": {
"command": "node",
"args": [
"\\\\wsl$\\Ubuntu\\home\\walmsles\\dev\\serverless-dna\\powertools-mcp\\dist\\bundle.js"
]
}
}
}服务器为每个支持的运行时加载预先构建的 lunr.js 索引
当收到搜索请求时，它会：
根据运行时和版本加载适当的索引（当前固定为最新版本）
使用 lunr.js 执行搜索
以 JSON 格式返回搜索结果
然后，法学硕士可以使用这些结果来查找相关的文档页面
麻省理工学院
Fetches the content of a Powertools documentation page and returns it as markdown. This allows you to read the full documentation for a specific feature or function. You MUST use the url returned form the search_docs tool since this will be the page to load.The URL must be from the docs.powertools.aws.dev domain. Use this after finding relevant pages with search_docs to get detailed information.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses that it fetches and returns content as markdown, but lacks details on error handling, rate limits, authentication needs, or response format beyond markdown. It adds some context about domain restriction and dependency on search_docs, but behavioral traits are incomplete.
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 the core purpose, followed by usage rules. Each sentence adds value: first defines the action, second explains the output format and use case, third specifies the input source and domain restriction, fourth provides the workflow context. No wasted words.
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 1 parameter with 0% schema coverage and no output schema, the description does well by explaining parameter semantics and usage context. It covers the tool's role in the workflow with search_docs. However, without annotations or output schema, it lacks details on errors, performance, or exact return structure, leaving some gaps.
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 0%, so the description must compensate. It explains that the 'url' parameter must be from the docs.powertools.aws.dev domain and obtained from the search_docs tool, adding crucial semantic context beyond the schema's basic URI type. However, it doesn't detail URL format or validation rules.
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 verb ('fetches') and resource ('content of a Powertools documentation page'), specifying it returns markdown. It distinguishes from its sibling 'search_docs' by explaining that this tool is for detailed content retrieval after search results are obtained.
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?
It provides explicit guidance on when to use this tool ('after finding relevant pages with search_docs') and when not to use it (the URL must be from docs.powertools.aws.dev domain, not arbitrary URLs). It names the alternative tool ('search_docs') and specifies the prerequisite input source.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Perform a search of the Powertools for AWS Lambda documentation index to find web page references online. Great for finding more details on Powertools features and functions using text search. Try searching for features like 'Logger', 'Tracer', 'Metrics', 'Idempotency', 'batchProcessor', etc. Powertools is available for the following runtimes: python, typescript, java, dotnet. If a specific version is not mentioned the search service will use the latest documentation.
| Name | Required | Description | Default |
|---|---|---|---|
| runtime | Yes | ||
| search | Yes | ||
| version | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that the search service defaults to the latest documentation if no version is specified, which adds useful context. However, it doesn't cover other behavioral aspects like rate limits, authentication needs, or what the output looks like (e.g., search results format).
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 the core purpose and efficiently provides examples and runtime details in subsequent sentences. It avoids unnecessary fluff, though the last sentence about version defaults could be integrated more seamlessly. Overall, it's well-structured and concise.
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 moderate complexity (3 parameters, no output schema, no annotations), the description covers the basic purpose and some parameter context but lacks details on output format, error handling, or advanced usage scenarios. It's adequate for a simple search tool but has clear gaps in completeness.
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 schema description coverage is 0%, so the description must compensate. It explains the 'runtime' parameter by listing supported runtimes (python, typescript, java, dotnet) and the 'version' parameter by noting it defaults to latest if unspecified. However, it doesn't clarify the 'search' parameter beyond examples, leaving its semantics partially undefined.
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: 'Perform a search of the Powertools for AWS Lambda documentation index to find web page references online.' It specifies the resource (Powertools documentation) and action (search), though it doesn't explicitly differentiate from the sibling 'fetch_doc_page' tool. The description provides helpful examples of search terms, which enhances clarity.
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 implies usage by suggesting search terms ('Logger', 'Tracer', etc.) and mentioning runtime support, but it doesn't explicitly state when to use this tool versus 'fetch_doc_page' or provide any exclusion criteria. It offers some contextual guidance but lacks clear alternatives or prerequisites.
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.
v1.0.0fetch_doc_pagesearch_docsScored across 2 tools
The two tools have clearly distinct purposes: search_docs finds relevant documentation pages, while fetch_doc_page retrieves the content of a specific page. There is no overlap in functionality, and the descriptions explicitly state the workflow relationship between them.
Both tools follow a consistent verb_noun pattern (search_docs, fetch_doc_page) with clear, descriptive names that match their functions. The naming is uniform and predictable throughout the set.
With only 2 tools, the server feels thin for a documentation search domain. While the tools cover the core search-and-retrieve workflow, additional utilities like listing available versions or filtering by runtime could enhance completeness. The count is borderline but functional.
The toolset covers the essential documentation search workflow: finding pages and fetching content. However, there are minor gaps, such as no direct way to list documentation sections or filter searches by runtime/version without relying on search queries. Agents can work around these with the provided tools.
MCP server for langchain documentation, generated by doc2mcp.
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…
Enable secure connectivity between Sentry issues and debugging data, and LLM clients, using a Model Context Protocol (MCP) 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.
