docker-mcp
🐳 docker-mcp
Un potente servidor de Protocolo de Contexto de Modelo (MCP) para operaciones de Docker, que permite una gestión fluida de contenedores y pilas de composición a través de Claude AI.
✨ Características
🚀 Creación e instanciación de contenedores
Implementación de la pila Docker Compose
Recuperación de registros de contenedores
📊 Listado de contenedores y monitoreo del estado
🎬 Demos
Implementación de una pila Docker Compose
https://github.com/user-attachments/assets/b5f6e40a-542b-4a39-ba12-7fdf803ee278
Análisis de registros de contenedores
https://github.com/user-attachments/assets/da386eea-2fab-4835-82ae-896de955d934
Related MCP server: MCP Development Server
🚀 Inicio rápido
Para probar esto en la aplicación Claude Desktop, agregue esto a sus archivos de configuración de Claude:
{
"mcpServers": {
"docker-mcp": {
"command": "uvx",
"args": [
"docker-mcp"
]
}
}
}Instalación mediante herrería
Para instalar Docker MCP para Claude Desktop automáticamente a través de Smithery :
npx @smithery/cli install docker-mcp --client claudePrerrequisitos
UV (gestor de paquetes)
Python 3.12+
Docker Desktop o Docker Engine
Escritorio de Claude
Instalación
Configuración del escritorio de Claude
Agregue la configuración del servidor a su archivo de configuración de Claude Desktop:
MacOS : ~/Library/Application\ Support/Claude/claude_desktop_config.json
Ventanas : %APPDATA%/Claude/claude_desktop_config.json
{
"mcpServers": {
"docker-mcp": {
"command": "uv",
"args": [
"--directory",
"<path-to-docker-mcp>",
"run",
"docker-mcp"
]
}
}
}{
"mcpServers": {
"docker-mcp": {
"command": "uvx",
"args": [
"docker-mcp"
]
}
}
}🛠️ Desarrollo
Configuración local
Clonar el repositorio:
git clone https://github.com/QuantGeekDev/docker-mcp.git
cd docker-mcpCrear y activar un entorno virtual:
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activateInstalar dependencias:
uv sync🔍 Depuración
Inicie el Inspector MCP para depurar:
npx @modelcontextprotocol/inspector uv --directory <path-to-docker-mcp> run docker-mcpEl Inspector proporcionará una URL para acceder a la interfaz de depuración.
📝 Herramientas disponibles
El servidor proporciona las siguientes herramientas:
crear contenedor
Crea un contenedor Docker independiente
{
"image": "image-name",
"name": "container-name",
"ports": {"80": "80"},
"environment": {"ENV_VAR": "value"}
}implementar-componer
Implementa una pila de Docker Compose
{
"project_name": "example-stack",
"compose_yaml": "version: '3.8'\nservices:\n service1:\n image: image1:latest\n ports:\n - '8080:80'"
}obtener registros
Recupera registros de un contenedor específico
{
"container_name": "my-container"
}contenedores de lista
Enumera todos los contenedores Docker
{}🚧 Limitaciones actuales
No hay soporte de variables de entorno integradas para contenedores
Sin gestión de volumen
Sin gestión de red
No se realizan controles de salud de los contenedores
No hay políticas de reinicio de contenedores
Sin límites de recursos de contenedores
🤝 Contribuyendo
Bifurcar el repositorio desde docker-mcp
Crea tu rama de funciones
Confirme sus cambios
Empujar hacia la rama
Abrir una solicitud de extracción
📜 Licencia
Este proyecto está licenciado bajo la licencia MIT: consulte el archivo de LICENCIA para obtener más detalles.
✨ Autores
Alex Andru - Trabajo inicial | Colaborador principal - @QuantGeekDev
Ali Sadykov - Trabajo inicial | Colaborador principal - @md-archive
Hecho con ❤️
Available Tools
4 toolscreate-containerC
Create a new standalone Docker container
| Name | Required | Description | Default |
|---|---|---|---|
| image | Yes | ||
| name | No | ||
| ports | No | ||
| environment | No |
TDQS
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 states 'Create' implying a mutation operation but doesn't cover permissions, side effects, error handling, or response format. For a tool that likely requires Docker daemon access and creates persistent resources, this is a significant gap in transparency.
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 gets straight to the point with zero wasted words. It's appropriately sized for a basic tool definition and front-loaded with the core action.
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 complexity (4 parameters with nested objects, no annotations, no output schema), the description is incomplete. It doesn't address parameter meanings, behavioral traits, or output expectations, leaving the agent with insufficient context to use the tool effectively beyond the basic purpose.
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 but adds no parameter information. It doesn't explain what 'image', 'name', 'ports', or 'environment' mean in the Docker context, their formats, or examples. With 4 parameters and nested objects, this leaves critical usage details undocumented.
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 ('Create') and resource ('new standalone Docker container'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'deploy-compose' which might also create containers, missing the 'standalone' distinction that could be more explicit.
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?
No guidance is provided on when to use this tool versus alternatives like 'deploy-compose' for multi-container setups or 'list-containers' for viewing existing ones. The description lacks context about prerequisites or typical scenarios for standalone container creation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
deploy-composeC
Deploy a Docker Compose stack
| Name | Required | Description | Default |
|---|---|---|---|
| compose_yaml | Yes | ||
| project_name | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers minimal behavioral insight. It states 'Deploy' implies a write/mutation operation, but doesn't disclose critical traits like whether it's idempotent, requires specific permissions, destroys existing resources, handles errors, or has rate limits. The description adds no 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with a single, front-loaded sentence that directly states the tool's purpose. There is zero wasted text, and it efficiently communicates the core function without unnecessary elaboration.
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 complexity of deployment operations (mutating system state), no annotations, no output schema, and 0% schema coverage for 2 parameters, the description is incomplete. It lacks essential details like what 'deploy' entails behaviorally, parameter meanings, expected outcomes, or error handling, leaving significant gaps for an AI agent.
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 but provides no parameter information. It doesn't explain what 'compose_yaml' should contain (e.g., YAML string format), what 'project_name' is used for (e.g., naming containers/networks), or any constraints (e.g., length, characters). With 2 undocumented parameters, this is inadequate.
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 ('Deploy') and resource ('a Docker Compose stack'), making the purpose immediately understandable. It distinguishes from siblings like 'create-container' (single container vs. stack) and 'get-logs'/'list-containers' (read operations vs. deployment). However, it doesn't specify what 'deploy' entails (e.g., creating containers, networks, volumes) beyond the high-level concept.
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?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., Docker Compose installed), when to choose this over 'create-container' for single-container deployments, or any constraints (e.g., environment compatibility). Usage is implied from the name but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get-logsC
Retrieve the latest logs for a specified Docker container
| Name | Required | Description | Default |
|---|---|---|---|
| container_name | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden but only states the action without behavioral details. It doesn't disclose what 'latest logs' means (e.g., time range, log count, format), whether it's read-only or has side effects, or any constraints like rate limits or authentication needs.
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 with no wasted words, front-loading the core action and resource. It's appropriately sized for a simple tool with one parameter.
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, no output schema, and low schema coverage, the description is incomplete. It lacks details on return values (e.g., log format, structure), error handling, and behavioral context needed for a logging tool in a Docker environment with siblings.
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%, but the description adds meaning by specifying that 'container_name' is for a Docker container. However, it doesn't clarify parameter details like format, examples, or how it relates to other tools (e.g., if it must match names from 'list-containers'), leaving gaps.
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 'retrieve' and resource 'latest logs for a specified Docker container', making the purpose evident. It distinguishes from siblings like 'list-containers' by focusing on logs rather than container metadata, though it doesn't explicitly contrast with them.
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?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., container must be running), exclusions, or comparisons with sibling tools like 'list-containers' for container discovery.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list-containersB
List all Docker containers
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
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 states the action ('List all') but lacks details on permissions, rate limits, output format, or any constraints (e.g., whether it includes stopped containers). This is a significant gap for a tool with zero 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words. It is front-loaded and directly states the tool's purpose, making it highly concise and well-structured for quick understanding.
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 lack of annotations and output schema, the description is incomplete. It fails to provide necessary behavioral context (e.g., what 'all' entails, response format) or usage guidelines, making it inadequate for a tool that might have hidden complexities despite having no parameters.
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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, but this is acceptable given the schema's completeness, aligning with the baseline of 4 for zero parameters.
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 'List all Docker containers' clearly states the verb ('List') and resource ('Docker containers'), making the purpose immediately understandable. However, it doesn't differentiate from potential sibling tools like 'get-logs' which might also list containers but with logs, so it misses full sibling distinction.
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 like 'create-container' or 'deploy-compose'. There is no mention of context, prerequisites, or exclusions, leaving the agent to infer usage based on tool names alone.
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.
4 tool updates
v1.0.0- Added
create-container - Added
deploy-compose - Added
get-logs - Added
list-containers
TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose with no overlap: create-container for creating containers, deploy-compose for deploying stacks, get-logs for retrieving logs, and list-containers for listing containers. The descriptions make it easy to tell them apart.
All tool names follow a consistent verb_noun pattern (e.g., create-container, deploy-compose, get-logs, list-containers). There are no deviations in naming style or conventions.
With 4 tools, the count is reasonable for a Docker MCP server, covering key operations like creating, listing, and managing containers and stacks. It's slightly lean but well-scoped for basic Docker management tasks.
The toolset covers core operations (create, list, logs, deploy) but has notable gaps, such as missing update, delete, or stop/start container tools, which could limit agent workflows for full container lifecycle management.
Maintenance
Related MCP Connectors
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…
MCP server unifying ERPs, CRMs, APIs and knowledge base for Claude, ChatGPT and Gemini.
MCP server for AI agents to plan, verify, and deploy Cloudflare-native apps.
Remote MCP server exposing your Node Command fleet (files, shell, processes, browser) to AI hosts.
Related MCP Servers
- FlicenseAqualityFmaintenanceA Model Context Protocol server that enables Docker container management through natural language interactions using a custom GPT interface.715-
- FlicenseNot gradedqualityDmaintenanceA Model Context Protocol server that enables Claude to manage software development projects with complete context awareness and code execution through Docker environments.18 npm4-
- AlicenseAqualityFmaintenanceAllows Claude and other AI assistants to interact with Docker through the MCP protocol, enabling container and image management including listing, running, stopping, and pulling Docker resources.676 npm4MIT
- FlicenseNot gradedqualityDmaintenanceA TypeScript server that fully implements the Model Context Protocol (MCP) standard, providing API access to Docker CLI operations like build, run, stop, and image management through compatible AI clients.-
Appeared in Searches
- Managing Docker Containers and Applications
- Information about Docker software and containerization
- A server for managing Minecraft Fabric modpacks using Claude
- A server that can run Docker Compose commands to manage containers
- How to retrieve information about a device using Microsoft Defender, Intune, and Jamf