Ein Model Context Protocol (MCP)-Server, der Suchfunktionen für die AWS Lambda Powertools-Dokumentation über mehrere Laufzeiten hinweg bereitstellt.
Befolgen Sie die Installationsanweisungen. Bitte folgen Sie der Schnellstartanleitung zum Model Context Protocol für Claude Desktop-Benutzer . Sie müssen der MCP-Konfigurationsdatei einen Abschnitt wie folgt hinzufügen:
{
"mcpServers": {
"powertools": {
"command": "npx",
"args": [
"-y",
"@serverless-dna/powertools-mcp"
]
}
}
}Related MCP server: MCP-Ragdocs
Dieses Projekt implementiert einen MCP-Server, der Large Language Models (LLMs) die Suche in der AWS Lambda Powertools-Dokumentation ermöglicht. Es nutzt lunr.js für effiziente lokale Suchfunktionen und liefert Ergebnisse, die zusammengefasst und Benutzern präsentiert werden können.
MCP-kompatibler Server zur Integration mit LLMs
Lokale Suche mit lunr.js-Indizes
Unterstützung für mehrere Laufzeiten:
Python
Typoskript
Java
.NETTO
Versionsspezifische Dokumentationssuche (standardmäßig die neueste Version)
# Install dependencies
pnpm install
# Build the project
pnpm buildDer Server kann als MCP-Server ausgeführt werden, der über stdio kommuniziert:
npx -y @serverless-dna/powertools-mcpDer Server stellt ein search_docs Tool mit den folgenden Parametern bereit:
search : Die Suchabfragezeichenfolge
runtime : Die zu durchsuchende Powertools-Laufzeit (Python, Typescript, Java, Dotnet)
version : Optionale Versionszeichenfolge (standardmäßig „neueste“)
src/ : Quellcode
index.ts : Hauptserverimplementierung
searchIndex.ts : Suchindexverwaltung
indexes/ : Vorgefertigte lunr.js-Suchindizes für jede Laufzeit
dist/ : Kompilierte Ausgabe
pnpm buildpnpm testWährend der Entwicklung können Sie den MCP-Server mit Claude Desktop mit der folgenden Konfiguration ausführen.
Die folgende Konfiguration zeigt die Ausführung in Windows Claude Desktop während der Entwicklung mit dem Windows-Subsystem für Linux (WSL). Mac- oder Linux-Umgebungen können auf ähnliche Weise ausgeführt werden.
Die Ausgabe ist eine gebündelte Datei, die es dem unter Windows installierten Node ermöglicht, den MCP-Server auszuführen, da alle Abhängigkeiten gebündelt sind.
{
"mcpServers": {
"powertools": {
"command": "node",
"args": [
"\\\\wsl$\\Ubuntu\\home\\walmsles\\dev\\serverless-dna\\powertools-mcp\\dist\\bundle.js"
]
}
}
}Der Server lädt vorgefertigte lunr.js-Indizes für jede unterstützte Laufzeit
Wenn eine Suchanfrage eingeht, geschieht Folgendes:
Lädt den entsprechenden Index basierend auf Laufzeit und Version (derzeit auf die neueste Version festgelegt)
Führt die Suche mit lunr.js durch
Gibt die Suchergebnisse als JSON zurück
Der LLM kann diese Ergebnisse dann verwenden, um relevante Dokumentationsseiten zu finden
MIT
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.
