de en es ja ko ru zh

steampipe-mcp

Name: steampipe-mcp
Author: turbot

by turbot

Overview Schema Source Related Servers Score Discussions Admin

TypeScript

Hybrid

Servidor de protocolo de contexto de modelo (MCP) de Steampipe

¡Desbloquea el poder del análisis de infraestructura basado en IA con Steampipe ! Este servidor de Protocolo de Contexto de Modelo conecta a la perfección asistentes de IA como Claude con los datos de tu infraestructura en la nube, lo que permite la exploración y el análisis en lenguaje natural de toda tu infraestructura en la nube.

Steampipe MCP conecta los asistentes de IA con los datos de su infraestructura, lo que permite el lenguaje natural:

Consultas en AWS, Azure, GCP y más de 100 servicios en la nube
Análisis de seguridad y cumplimiento
Optimización de costos y recursos
Asistencia para el desarrollo de consultas

Funciona tanto con instalaciones locales de Steampipe como con espacios de trabajo de Turbot Pipes , proporcionando acceso seguro y de solo lectura a todos sus datos en la nube y SaaS.

Instalación

Prerrequisitos

Node.js v16 o superior (incluye npx )
Para uso local: Steampipe instalado y en funcionamiento ( steampipe service start )
Para Turbot Pipes: un espacio de trabajo de Turbot Pipes y una cadena de conexión

Configuración

Añade Steampipe MCP al archivo de configuración de tu asistente de IA:

{
  "mcpServers": {
    "steampipe": {
      "command": "npx",
      "args": [
        "-y",
        "@turbot/steampipe-mcp"
      ]
    }
  }
}

De forma predeterminada, se conecta a tu instalación local de Steampipe en postgresql://steampipe@localhost:9193/steampipe . Asegúrate de ejecutar primero steampipe service start .

Para conectarse a un espacio de trabajo de Turbot Pipes , agregue su cadena de conexión a los argumentos:

{
  "mcpServers": {
    "steampipe": {
      "command": "npx",
      "args": [
        "-y",
        "@turbot/steampipe-mcp",
        "postgresql://my_name:my_pw@workspace-name.usea1.db.pipes.turbot.com:9193/abc123"
      ]
    }
  }
}

Configuración del asistente de IA

Asistente	Ubicación del archivo de configuración	Guía de configuración
Escritorio de Claude	`claude_desktop_config.json`	Guía del MCP de Claude Desktop →
Cursor	`~/.cursor/mcp.json`	Guía del cursor MCP →

Guarde el archivo de configuración y reinicie su asistente de IA para que los cambios surtan efecto.

Related MCP server: tailpipe-mcp

Guía de indicaciones

Primero, ejecuta el mensaje best_practices incluido en el servidor MCP para enseñarle a tu LLM cómo trabajar mejor con Steampipe. ¡Luego, pregúntale lo que quieras!

Explora tu infraestructura en la nube:

What AWS accounts can you see?

Las preguntas simples y específicas funcionan bien:

Show me all S3 buckets that were created in the last week

Generar informes de infraestructura:

List my EC2 instances with their attached EBS volumes

Profundice en el análisis de seguridad:

Find any IAM users with access keys that haven't been rotated in the last 90 days

Obtenga información sobre cumplimiento:

Show me all EC2 instances that don't comply with our tagging standards

Explorar los riesgos potenciales:

Analyze my S3 buckets for security risks including public access, logging, and encryption

Recuerde:

Sea específico sobre qué recursos de la nube desea analizar (EC2, S3, IAM, etc.)
Mencione regiones o cuentas si está interesado en algunas específicas.
Comience con consultas simples antes de agregar condiciones complejas
Utilice lenguaje natural: el LLM se encargará de la traducción SQL
Sea audaz y explorador: ¡el LLM puede ayudarle a descubrir conocimientos en toda su infraestructura!

Capacidades

Herramientas

consulta de tubería de vapor
- Consultar registros de seguridad y de la nube con SQL.
- Para obtener un mejor rendimiento: utilice CTE en lugar de uniones y limite las columnas solicitadas.
- Todas las consultas son de solo lectura y utilizan la sintaxis PostgreSQL.
- Entrada: sql (cadena): La consulta SQL que se ejecutará utilizando la sintaxis PostgreSQL
lista_de_tablas_de_steampipe
- Enumere todas las tablas Steampipe disponibles.
- Entrada opcional: schema (cadena): Filtrar tablas por esquema específico
- Entrada opcional: filter (cadena): Filtrar tablas por patrón ILIKE (por ejemplo, '%ec2%')
presentación de tabla de steampipe
- Obtenga información detallada sobre una tabla específica, incluidas definiciones de columnas, tipos de datos y descripciones.
- Entrada: name (cadena): el nombre de la tabla para la que se mostrarán detalles (puede estar calificado por el esquema, por ejemplo, 'aws_account' o 'aws.aws_account')
- Entrada opcional: schema (cadena): El esquema que contiene la tabla
lista de complementos de steampipe
- Enumera todos los complementos de Steampipe instalados en el sistema. Los complementos proporcionan acceso a diferentes fuentes de datos como AWS, GCP o Azure.
- No se requieren parámetros de entrada
presentación del complemento steampipe
- Obtenga detalles para una instalación específica de un complemento de Steampipe, incluida la versión, los límites de memoria y la configuración.
- Entrada: name (cadena): Nombre del complemento para el que se mostrarán detalles

Indicaciones

mejores prácticas
- Mejores prácticas para trabajar con datos de Steampipe
- Proporciona orientación detallada sobre:
  - Estilo de respuesta y convenciones de formato
  - Uso de CTE (cláusulas WITH) frente a uniones
  - Convenciones de sintaxis y estilo de SQL
  - Selección y optimización de columnas
  - Exploración y comprensión de esquemas
  - Estructura y organización de la consulta
  - Consideraciones de rendimiento y almacenamiento en caché
  - Manejo de errores y resolución de problemas

Recursos

estado
- Representa el estado actual de la conexión Steampipe
- Las propiedades incluyen:
  - connection_string: La cadena de conexión a la base de datos actual
  - estado: El estado de la conexión (conectado/desconectado)

Este recurso permite que las herramientas de IA verifiquen el estado de la conexión a su instancia de Steampipe.

Desarrollo

Clonar y configurar

Clona el repositorio y navega hasta el directorio:

git clone https://github.com/turbot/steampipe-mcp.git
cd steampipe-mcp

Instalar dependencias:

npm install

Construir el proyecto:

npm run build

Pruebas

Para probar su compilación de desarrollo local con herramientas de IA compatibles con MCP, actualice la configuración de MCP para usar el dist/index.js local en lugar del paquete npm. Por ejemplo:

{
  "mcpServers": {
    "steampipe": {
      "command": "node",
      "args": [
        "/absolute/path/to/steampipe-mcp/dist/index.js",
        "postgresql://steampipe@localhost:9193/steampipe"
      ]
    }
  }
}

O utilice el Inspector MCP para validar la implementación del servidor:

npx @modelcontextprotocol/inspector dist/index.js

Variables de entorno

Las siguientes variables de entorno se pueden utilizar para configurar el servidor MCP:

STEAMPIPE_MCP_LOG_LEVEL : Controla el nivel de detalle del registro del servidor (predeterminado: info )
STEAMPIPE_MCP_WORKSPACE_DATABASE : Anula la cadena de conexión predeterminada de Steampipe (predeterminada: postgresql://steampipe@localhost:9193/steampipe )

Código abierto y contribución

Este repositorio se publica bajo la licencia Apache 2.0 . Consulte nuestro código de conducta . ¡Esperamos colaborar con usted!

Steampipe es un producto desarrollado a partir de este software de código abierto, exclusivamente por Turbot HQ, Inc. Se distribuye bajo nuestros términos comerciales. Otros pueden crear su propia distribución del software, pero no pueden usar las marcas registradas, servicios en la nube, etc. de Turbot. Puede obtener más información en nuestras Preguntas frecuentes sobre código abierto .

Complicarse

Únete a #steampipe en Slack →

¿Quieres ayudar pero no sabes por dónde empezar? Elige uno de los temas help wanted :

Available Tools

5 tools

steampipe_plugin_listA

List all Steampipe plugins installed on the system. Plugins provide access to different data sources like AWS, GCP, or Azure.

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

A3.9/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It describes what the tool does (lists plugins) and provides useful context about plugins providing access to data sources, but doesn't disclose behavioral traits like whether this requires specific permissions, how results are formatted, or if there are any rate limits. It adds some value but lacks operational details.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences with zero waste. First sentence states the core purpose, second adds helpful context about plugins. Well-structured and front-loaded with the essential information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations, no output schema, and moderate complexity (listing operation), the description is adequate but incomplete. It explains what the tool does and provides context about plugins, but lacks details about return format, error conditions, or operational constraints that would be helpful for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters with 100% schema description coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, maintaining focus on the tool's purpose. Baseline for 0 parameters is 4, as it avoids unnecessary parameter discussion.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('List all') and resource ('Steampipe plugins installed on the system'), with additional context about what plugins provide. It distinguishes from siblings like steampipe_plugin_show (detail view) and steampipe_table_list (different resource type).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when needing to see installed plugins, but doesn't explicitly state when to use this vs. alternatives like steampipe_plugin_show for detailed plugin information or steampipe_table_list for table listings. No explicit exclusions or prerequisites are mentioned.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

steampipe_plugin_showA

Get details for a specific Steampipe plugin installation, including version, memory limits, and configuration.

ParametersJSON Schema

Name	Required	Description	Default
`name`	Yes	Name of the plugin to show details for

TDQS

A3.7/5.0

Behavior2/5

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 what details are returned (version, memory limits, configuration) but does not cover other important aspects such as error handling (e.g., what happens if the plugin doesn't exist), permissions required, or whether it's a read-only operation. This leaves significant gaps for a tool with no annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence that efficiently conveys the tool's purpose and key details. It is front-loaded with the main action and resource, and every part of the sentence adds value without redundancy or unnecessary information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (simple read operation with one parameter) and the lack of annotations and output schema, the description is moderately complete. It specifies what details are retrieved, but it does not fully compensate for the missing behavioral context (e.g., error handling, permissions) or describe the return format, which would be needed for higher completeness in the absence of an output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, with the parameter 'name' clearly documented in the schema as 'Name of the plugin to show details for.' The description does not add any additional meaning or context beyond what the schema provides, such as format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('Get details') and resource ('a specific Steampipe plugin installation'), distinguishing it from siblings like 'steampipe_plugin_list' (which lists all plugins) and 'steampipe_table_show' (which shows table details). It explicitly mentions what details are included: version, memory limits, and configuration.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context by specifying 'a specific Steampipe plugin installation,' suggesting it should be used when details for a particular plugin are needed. However, it does not explicitly state when not to use it or name alternatives like 'steampipe_plugin_list' for listing all plugins, which would be required for a score of 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

steampipe_queryA

Query cloud infrastructure, SaaS, APIs, code and more with SQL.

Queries are read-only and must use PostgreSQL syntax.

For best performance: limit columns requested, use materialized CTEs instead of joins. Trust the search path unless sure you need to specify a schema.

Check available tables and columns before querying using steampipe_table_list and steampipe_table_show.

ParametersJSON Schema

Name	Required	Description	Default
`sql`	Yes	The SQL query to execute. Must use PostgreSQL syntax and be read-only.

TDQS

A4.2/5.0

Behavior4/5

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 effectively describes key traits: queries are 'read-only,' must use 'PostgreSQL syntax,' and includes performance tips (limit columns, use CTEs, trust search path). This covers safety, syntax, and optimization, though it lacks details on error handling or output format.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and front-loaded, starting with the core purpose. Each sentence adds value: purpose, constraints, performance tips, and sibling tool references. There is no wasted text, making it efficient and easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (querying multiple data sources) and lack of annotations/output schema, the description does a good job covering key aspects: purpose, constraints, performance, and sibling tool integration. However, it doesn't explain return values or error cases, leaving some gaps for a tool with no output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, with the parameter 'sql' fully documented in the schema. The description adds minimal semantics beyond this, only reiterating that SQL must be 'read-only' and use 'PostgreSQL syntax,' which is already in the schema. This meets the baseline for high schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Query cloud infrastructure, SaaS, APIs, code and more with SQL.' It specifies the verb ('query') and resources, but doesn't explicitly differentiate from sibling tools like steampipe_table_list/show, which are mentioned for checking tables rather than querying. This makes it clear but not fully sibling-distinguished.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

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 alternatives: it instructs to 'Check available tables and columns before querying using steampipe_table_list and steampipe_table_show.' This clearly defines a usage sequence and distinguishes it from sibling tools, offering practical alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

steampipe_table_listB

List all available Steampipe tables. Use schema and filter parameters to narrow down results.

ParametersJSON Schema

Name	Required	Description	Default
`schema`	No	Optional schema name to filter tables by. If not provided, lists tables from all schemas.
`filter`	No	Optional filter pattern to match against table names. Use ILIKE syntax, including % as a wildcard.

TDQS

B3.3/5.0

Behavior2/5

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 that the tool 'lists' tables, which implies a read-only operation, but doesn't specify whether this requires authentication, how results are returned (e.g., pagination, format), or any rate limits. The description adds minimal behavioral context beyond the basic action.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise with just two sentences that are front-loaded and waste-free. The first sentence states the core purpose, and the second adds essential usage guidance, making every word earn its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (2 optional parameters, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and hints at parameter usage but lacks details on behavioral aspects like authentication, result format, or error handling, which would be helpful for an agent to use it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description mentions that 'schema and filter parameters' can be used to 'narrow down results,' which adds some context about their purpose. However, with 100% schema description coverage, the input schema already fully documents both parameters, including their types, optionality, and usage details (e.g., ILIKE syntax for filter). The description provides only marginal value beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('List all available Steampipe tables') and the resource ('Steampipe tables'), making the purpose immediately understandable. However, it doesn't explicitly differentiate this tool from its sibling 'steampipe_table_show' which likely shows details of a specific table rather than listing all tables.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides implied usage guidance by mentioning that schema and filter parameters can be used to 'narrow down results,' suggesting this tool is for listing tables with optional filtering. However, it doesn't explicitly state when to use this tool versus alternatives like 'steampipe_table_show' or 'steampipe_query,' nor does it provide exclusion criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

steampipe_table_showB

Get detailed information about a specific Steampipe table, including column definitions, data types, and descriptions.

ParametersJSON Schema

Name	Required	Description	Default
`name`	Yes	The name of the table to show details for. Can be schema qualified (e.g. 'aws_account' or 'aws.aws_account').
`schema`	No	Optional schema name. If provided, only searches in this schema. If not provided, searches across all schemas.

TDQS

B3.3/5.0

Behavior2/5

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 describes the tool's function but lacks details on behavioral traits such as error handling (e.g., what happens if the table doesn't exist), performance characteristics, or output format. This is a significant gap for a tool with no annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence that efficiently conveys the tool's purpose and scope without unnecessary words. It's front-loaded with the main action and resource, making it easy to understand at a glance.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is minimally adequate. It covers the purpose but lacks behavioral context and output details, which are important for a tool that retrieves metadata. Without annotations or output schema, more completeness would be beneficial.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

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 both parameters ('name' and 'schema') with clear descriptions. The description doesn't add any parameter-specific information beyond what's in the schema, such as examples or constraints. Baseline 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('Get detailed information') and resource ('specific Steampipe table'), and specifies the scope of information returned ('including column definitions, data types, and descriptions'). It distinguishes from the sibling 'steampipe_table_list' by focusing on details for a single table rather than listing tables. However, it doesn't explicitly contrast with 'steampipe_query' which might also return table information through queries.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage when detailed metadata about a specific table is needed, but doesn't provide explicit guidance on when to use this tool versus alternatives like 'steampipe_table_list' for listing tables or 'steampipe_query' for querying data. It mentions the resource type ('Steampipe table') but lacks context on prerequisites or exclusions.

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.

5 tool updatesAug 14, 2025
- First observedsteampipe_plugin_list
- First observedsteampipe_plugin_show
- First observedsteampipe_query
- First observedsteampipe_table_list
- First observedsteampipe_table_show

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A3.9/5.0

Scored 2026-04-18 10:20 across 5 tools

Disambiguation5/5

Each tool has a clearly distinct purpose with no overlap: listing plugins vs. showing plugin details vs. executing queries vs. listing tables vs. showing table details. The descriptions reinforce these distinctions, making misselection unlikely.

Naming Consistency5/5

All tools follow a consistent 'steampipe_' prefix with a clear verb_noun pattern (plugin_list, plugin_show, query, table_list, table_show). This uniformity makes the tool set predictable and easy to navigate.

Tool Count5/5

Five tools is well-scoped for the server's purpose of interacting with Steampipe. It covers plugin management, query execution, and table metadata without being overly sparse or bloated, with each tool earning its place.

Completeness4/5

The tool set provides strong coverage for querying and exploring Steampipe data, including plugin and table metadata. A minor gap exists in lacking direct plugin installation or configuration management tools, but agents can work around this using existing query capabilities.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

Need Help?

Related Servers

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Connectors

mcpOAuth
com.vibgrate
Query your team's drift, vulnerability, and upgrade data from any AI assistant. OAuth 2.1, 51 tools.
Security Monitoring
2
mcpOAuth
com.keboola
Connect your AI assistants to Keboola and expose your data, transformations, SQL queries, ...
Data Platforms Databases Developer Tools
286
CloudQuellOAuth
com.cloudquell
Connects AI assistants to CloudQuell multi-cloud and AI cost, savings, anomaly, and budget data.
Cloud Platforms Monitoring Finance
1
mcpmyadminOAuth
ai.mcpmyadmin
Query 40 databases from Claude, ChatGPT, or Cursor — on any device. Read-only, encrypted, audited.
Databases Security
2

Related MCP Servers

Supabase MCP Server
Cloud Platforms Databases Developer Tools
pipethedev
A
license
Not graded
quality
D
maintenance
Connects AI assistants to Supabase projects, enabling them to manage tables, query data, deploy Edge Functions, handle migrations, and access project resources through natural language commands.
Updated a year ago (2025-08-31 22:10 UTC)
Apache 2.0
tailpipe-mcpofficial
Monitoring Observability
turbot
A
license
Not graded
quality
D
maintenance
Connects AI assistants to cloud and SaaS logs via Tailpipe, enabling natural language querying and analysis of log data.
Updated 5 months ago (2026-03-26 17:06 UTC)
16
7
Apache 2.0
GraphJinofficial
Databases Search
dosco
A
license
Not graded
quality
A
maintenance
Enables AI assistants to query databases using natural language, with automatic schema discovery and SQL compilation.
Updated 2 days ago (2026-09-09 09:37 UTC)
995
3,162
Apache 2.0
Turbot Guardrails MCP Serverofficial
Cloud Platforms
turbot
A
license
Not graded
quality
C
maintenance
Connects AI assistants to Turbot Guardrails for natural language exploration, analysis, and automation of cloud governance.
Updated 4 months ago (2026-04-28 11:51 UTC)
15
4
Apache 2.0

Servidor de protocolo de contexto de modelo (MCP) de Steampipe

Instalación

Prerrequisitos

Configuración

Configuración del asistente de IA

Guía de indicaciones

Capacidades

Herramientas

Indicaciones

Recursos

Desarrollo

Clonar y configurar

Pruebas

Variables de entorno

Código abierto y contribución

Complicarse

Available Tools

TDQS

TDQS

TDQS

TDQS

TDQS

Tool Schema Changelog

TDQS

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

Supabase MCP Server

tailpipe-mcpofficial

GraphJinofficial

Turbot Guardrails MCP Serverofficial