AWS Knowledge Base Retrieval MCP Server
Servidor MCP de recuperación de la base de conocimientos de AWS
Una implementación de servidor MCP para recuperar información de la base de conocimientos de AWS mediante Bedrock Agent Runtime.
Características
RAG (Recuperación-Generación Aumentada) : recupera el contexto de la Base de conocimiento de AWS en función de una consulta y un ID de la Base de conocimiento.
Admite la recuperación de múltiples resultados : opción para recuperar una cantidad personalizable de resultados.
Related MCP server: DuckDB-RAG-MCP-Sample
Herramientas
recuperar_de_aws_kb
Realice operaciones de recuperación utilizando la base de conocimientos de AWS.
Entradas:
query(cadena): la consulta de búsqueda para la recuperación.knowledgeBaseId(cadena): el ID de la base de conocimiento de AWS.n(número, opcional): Número de resultados a recuperar (predeterminado: 3).
Formato de respuesta:
La respuesta ahora devuelve dos elementos de contenido separados:
Un elemento de texto que contiene el contexto sin procesar de la base de conocimientos.
Un elemento JSON que contiene las fuentes RAG estructuradas con metadatos (id, nombre de archivo, fragmento y puntuación).
Esta separación permite un procesamiento más flexible de los resultados.
Configuración
Configuración de credenciales de AWS
Tiene dos opciones para configurar las credenciales de AWS:
Opción 1: Claves de acceso de IAM
Obtenga la ID de clave de acceso de AWS, la clave de acceso secreta y la región desde la consola de administración de AWS.
Asegúrese de que estas credenciales tengan los permisos adecuados para las operaciones de Bedrock Agent Runtime.
Establezca las variables de entorno como se muestra en los ejemplos de configuración a continuación.
Para las credenciales temporales, también puede proporcionar un token de sesión utilizando la variable de entorno
AWS_SESSION_TOKEN.
Opción 2: AWS SSO (inicio de sesión único)
El servidor ahora admite las credenciales SSO de AWS:
Configure AWS CLI con su perfil SSO:
aws configure ssoEstablezca solo la variable de entorno AWS_REGION en la configuración del servidor MCP.
El servidor utilizará la cadena de proveedor de credenciales predeterminada, que incluye credenciales SSO.
Opcional: Configurar los ID de la base de conocimientos predeterminados
Opcionalmente, puede especificar uno o más identificadores de base de conocimiento para usar de manera predeterminada:
Cree una matriz de identificadores de bases de conocimiento en formato JSON.
Establezca esto como la variable de entorno AWS_KB_IDS en su configuración.
Cuando esto está configurado, el parámetro
knowledgeBaseIdse vuelve opcional en la herramienta.
Uso con Claude Desktop
Agregue esto a su claude_desktop_config.json :
Docker con claves de acceso IAM
{
"mcpServers": {
"aws-kb-retrieval": {
"command": "docker",
"args": [ "run", "-i", "--rm", "-e", "AWS_ACCESS_KEY_ID", "-e", "AWS_SECRET_ACCESS_KEY", "-e", "AWS_REGION", "-e", "AWS_KB_IDS", "mcp/aws-kb-retrieval-server" ],
"env": {
"AWS_ACCESS_KEY_ID": "YOUR_ACCESS_KEY_HERE",
"AWS_SECRET_ACCESS_KEY": "YOUR_SECRET_ACCESS_KEY_HERE",
"AWS_SESSION_TOKEN": "YOUR_OPTIONAL_SESSION_ID_FOR_SSO_TEMPORARY_CREDENTIALS_HERE",
"AWS_REGION": "YOUR_AWS_REGION_HERE",
"AWS_KB_IDS": "[\"kb-12345\", \"kb-67890\"]"
}
}
}
}Docker con AWS SSO
{
"mcpServers": {
"aws-kb-retrieval": {
"command": "docker",
"args": [ "run", "-i", "--rm", "-e", "AWS_REGION", "-e", "AWS_KB_IDS", "-v", "${HOME}/.aws:/root/.aws", "mcp/aws-kb-retrieval-server" ],
"env": {
"AWS_ACCESS_KEY_ID": "YOUR_ACCESS_KEY_HERE",
"AWS_SECRET_ACCESS_KEY": "YOUR_SECRET_ACCESS_KEY_HERE",
"AWS_SESSION_TOKEN": "YOUR_OPTIONAL_SESSION_ID_FOR_SSO_TEMPORARY_CREDENTIALS_HERE",
"AWS_REGION": "YOUR_AWS_REGION_HERE",
"AWS_KB_IDS": "[\"kb-12345\", \"kb-67890\"]"
}
}
}
}NPX con claves de acceso IAM
{
"mcpServers": {
"aws-kb-retrieval": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-aws-kb-retrieval"
],
"env": {
"AWS_ACCESS_KEY_ID": "YOUR_ACCESS_KEY_HERE",
"AWS_SECRET_ACCESS_KEY": "YOUR_SECRET_ACCESS_KEY_HERE",
"AWS_SESSION_TOKEN": "YOUR_OPTIONAL_SESSION_ID_FOR_SSO_TEMPORARY_CREDENTIALS_HERE",
"AWS_REGION": "YOUR_AWS_REGION_HERE",
"AWS_KB_IDS": "[\"kb-12345\", \"kb-67890\"]"
}
}
}
}NPX con AWS SSO
{
"mcpServers": {
"aws-kb-retrieval": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-aws-kb-retrieval"
],
"env": {
"AWS_ACCESS_KEY_ID": "YOUR_ACCESS_KEY_HERE",
"AWS_SECRET_ACCESS_KEY": "YOUR_SECRET_ACCESS_KEY_HERE",
"AWS_SESSION_TOKEN": "YOUR_OPTIONAL_SESSION_ID_FOR_SSO_TEMPORARY_CREDENTIALS_HERE",
"AWS_REGION": "YOUR_AWS_REGION_HERE",
"AWS_KB_IDS": "[\"kb-12345\", \"kb-67890\"]"
}
}
}
}Repositorio local (del repositorio clonado/construido)
{
"mcpServers": {
"aws-kb": {
"command": "node",
"args": [
"/path/to/mcp-aws-kb/dist/index.js"
],
"env": {
"AWS_ACCESS_KEY_ID": "YOUR_ACCESS_KEY_HERE",
"AWS_SECRET_ACCESS_KEY": "YOUR_SECRET_ACCESS_KEY_HERE",
"AWS_SESSION_TOKEN": "YOUR_OPTIONAL_SESSION_ID_FOR_SSO_TEMPORARY_CREDENTIALS_HERE",
"AWS_REGION": "YOUR_AWS_REGION_HERE",
"AWS_KB_IDS": "[\"kb-12345\", \"kb-67890\"]"
},
"disabled": false,
"autoApprove": [
"retrieve_from_aws_kb"
],
"timeout": 120
}
}
}Edificio
Estibador:
docker build -t mcp/aws-kb-retrieval -f src/aws-kb-retrieval-server/Dockerfile .Licencia
Este servidor MCP cuenta con la licencia MIT. Esto significa que puede usar, modificar y distribuir el software libremente, sujeto a los términos y condiciones de la licencia MIT. Para más detalles, consulte el archivo de LICENCIA en el repositorio del proyecto.
Este archivo README asume que el nombre del paquete de su servidor es @modelcontextprotocol/server-aws-kb-retrieval . Ajuste el nombre del paquete y los detalles de instalación si difieren en su configuración. Además, asegúrese de que el script de su servidor esté correctamente compilado y de que todas las dependencias se administren correctamente en su package.json .
Available Tools
1 toolretrieve_from_aws_kbC
Performs retrieval from the AWS Knowledge Base using the provided query and Knowledge Base ID.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The query to perform retrieval on | |
| knowledgeBaseId | Yes | The ID of the AWS Knowledge Base | |
| n | No | Number of results to retrieve |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions retrieval but fails to describe key traits like authentication requirements, rate limits, error handling, or what the retrieval entails (e.g., returns text snippets, metadata). This leaves significant gaps in understanding how the tool behaves in practice.
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, efficient sentence that directly states the tool's function and inputs without any fluff. It's front-loaded with the core purpose and appropriately sized for the task, making it easy to parse quickly.
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 no annotations and no output schema, the description is incomplete. It doesn't explain what the retrieval returns (e.g., format, content), error conditions, or operational details like latency or costs. For a tool with 3 parameters and missing structured data, this leaves the agent under-informed about critical context.
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 schema fully documents all parameters (query, knowledgeBaseId, n). The description adds no additional meaning beyond what's in the schema, such as examples or constraints. This meets the baseline for high schema coverage but doesn't enhance parameter understanding.
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 action ('performs retrieval') and the resource ('from the AWS Knowledge Base'), making the purpose immediately understandable. It specifies the required inputs (query and Knowledge Base ID), which helps distinguish it from generic retrieval tools. However, without sibling tools, it doesn't need to differentiate further, so it's not a perfect 5.
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, prerequisites, or contextual constraints. It merely states what the tool does without indicating appropriate scenarios or limitations, leaving the agent without usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
- First observed
retrieve_from_aws_kb
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined and distinct by default.
A single tool inherently has perfect naming consistency, as there are no other tools to compare it against for patterns or conventions.
One tool is too few for a server focused on AWS Knowledge Base retrieval, as it lacks essential operations like listing knowledge bases, managing queries, or handling configurations, making the scope feel incomplete and limiting.
The tool set is severely incomplete for the domain of AWS Knowledge Base operations. It only provides retrieval, missing critical functions such as creating, updating, or deleting knowledge base entries, which are necessary for a full workflow.
Maintenance
Related MCP Connectors
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.
MCP server for AI dialogue using various LLM models via AceDataCloud
Driflyte MCP server which lets AI assistants query topic-specific knowledge from web and GitHub.
Related MCP Servers
- AlicenseBqualityCmaintenanceAn MCP server that enables AI models to retrieve information from Ragie's knowledge base through a simple 'retrieve' tool.134 npm91MIT
- AlicenseNot gradedqualityDmaintenanceAn MCP server that enables RAG (Retrieval-Augmented Generation) on markdown documents by converting them to embedding vectors and performing vector search using DuckDB.4Apache 2.0
- FlicenseNot gradedqualityDmaintenanceAn MCP server that implements Retrieval-Augmented Generation to efficiently retrieve and process important information from various sources, providing accurate and contextually relevant responses.-
- FlicenseNot gradedqualityDmaintenanceAn MCP server integrating LangChain, RAG, and Agent to provide knowledge retrieval and tool invocation through natural language.-