Docker MCP Server
The Docker MCP Server is an AI-native Model Context Protocol server that enables AI assistants (like GitHub Copilot and Claude) to manage Docker resources through natural language commands.
Container Management
List all containers (running or all)
Create a container with full configuration: image, name, environment variables, commands, entrypoints, port mappings, volume bindings, and labels
Run a container (create + start in one step) โ the preferred method
Start, stop (with configurable timeout), restart, pause, unpause, and rename containers
Remove containers (with options to force or remove associated volumes)
Inspect containers and retrieve logs (with tail and follow options) and stats
Image Operations
List, pull, build, push, tag, remove, and prune Docker images
Network Management
List, create, inspect, connect/disconnect containers, remove, and prune networks
Volume Management
List, create, inspect, remove, and prune volumes
System & Connectivity
Get Docker system info and version details
Validate and test Docker connections
Connect to remote Docker hosts via TCP, HTTPS with TLS certificate authentication, or SSH tunnels
Provides tools for interacting with the Docker daemon to manage containers, images, networks, and volumes, including capabilities for container lifecycle management, log retrieval, and detailed resource inspection.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Docker MCP Serverlist all running containers and show their status"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Docker MCP Server
A comprehensive, production-ready, industry-standard compliant MCP (Model Context Protocol) Server that enables full Docker management capabilities for AI assistants like GitHub Copilot and Claude. Featuring 37 powerful tools covering containers, images, networks, volumes, and system operations.
๐ Table of Contents
Related MCP server: DockerHub MCP Server
About
Docker MCP Server is a Model Context Protocol server that bridges AI assistants with Docker, enabling natural language Docker operations. Built with TypeScript and following industry best practices, it provides a complete Docker management solution for AI-powered development workflows.
Why Docker MCP Server?
๐ค AI-Native: Designed specifically for AI assistants (GitHub Copilot, Claude)
๐ง Complete Coverage: 37 tools covering all essential Docker operations
๐ Remote Support: Connect to Docker on any host via TCP, HTTPS, or SSH tunnel
๐ Security First: Full TLS/SSL support with certificate authentication
๐ฆ Production Ready: Comprehensive error handling, type safety, and testing
๐ Well Documented: Extensive documentation with examples for every feature
๐ Easy to Use: Simple installation and configuration
Use Cases
AI-Assisted DevOps: Let AI assistants manage your Docker infrastructure
Container Orchestration: Create, manage, and monitor containers through natural language
Development Automation: Automate Docker workflows with AI assistance
Remote Management: Securely manage Docker on remote hosts
Learning & Exploration: Explore Docker capabilities with AI guidance
Quick Start
For npm Users (Recommended)
# Install globally
npm install -g @swartdraak/docker-mcp-server
# Or use with npx (no installation needed)
npx @swartdraak/docker-mcp-serverFor Developers
# Clone the repository
git clone https://github.com/Swartdraak/Docker-MCP.git
cd Docker-MCP
# Install dependencies
npm install
# Build the project
npm run build
# Start the server
npm startโจ Features
Core Capabilities
โ 37 Docker Tools: Complete coverage of Docker operations including connection validation
โ Remote Docker Support: Connect to Docker on remote hosts via TCP, HTTP, HTTPS, or SSH tunnel
โ Secure Connections: Full TLS/SSL support for secure remote Docker management
โ Container Management: Create, run, start, stop, restart, pause, unpause, rename, remove, exec, stats, logs
โ Image Operations: List, pull, build, push, tag, remove, prune
โ Network Management: List, create, remove, inspect, connect, disconnect
โ Volume Management: List, create, remove, inspect, prune
โ System Operations: Info, version, connection validation, prune (containers, images, volumes, networks)
โ Proper Array Handling: Correctly handles command, entrypoint, and environment variables
โ VS Code Integration: Works seamlessly with GitHub Copilot
โ Industry Standard: Uses MCP SDK and Docker best practices
โ TypeScript: Full type safety and modern JavaScript features
Recent Enhancements
๐ Remote Docker Host Support: Connect to Docker on any remote host
๐ TLS/HTTPS Support: Secure connections with certificate authentication
๐ Environment-based Configuration: Easy setup via DOCKER_HOST, DOCKER_TLS_VERIFY, DOCKER_CERT_PATH
๐ SSH Tunnel Support: Secure remote access without exposing Docker API
What's New in v2.0
๐ 25 New Tools: Added extensive container, image, network, and volume management
๐ง Container Exec: Execute commands in running containers
๐ Container Stats: Real-time CPU, memory, network, and I/O metrics
๐๏ธ Image Building: Build images from Dockerfile with build args
๐ Advanced Lifecycle: Restart, pause, unpause, rename containers
๐ Full Network CRUD: Create, inspect, connect, disconnect, remove networks
๐พ Full Volume CRUD: Create, inspect, remove volumes
๐งน Resource Cleanup: Prune unused containers, images, volumes, networks
โ๏ธ System Info: Get Docker daemon information and version
Installation
Via npm (Recommended)
npm install -g @swartdraak/docker-mcp-serverFrom Source
Prerequisites
Node.js 18 or higher
Docker installed and running
npm or yarn package manager
Setup
Clone the repository:
git clone https://github.com/Swartdraak/Docker-MCP.git
cd Docker-MCPInstall dependencies:
npm installBuild the project:
npm run buildUsage
Standalone Mode
Run the server directly:
npm startRemote Docker Configuration
The MCP server supports connecting to remote Docker hosts using environment variables:
Connect to Remote Docker via TCP
DOCKER_HOST=tcp://192.168.1.100:2375 npm startConnect to Remote Docker via HTTPS with TLS
DOCKER_HOST=https://192.168.1.100:2376 \
DOCKER_TLS_VERIFY=1 \
DOCKER_CERT_PATH=~/.docker/certs \
npm startConnect via SSH Tunnel
First, set up an SSH tunnel:
ssh -NL localhost:2375:/var/run/docker.sock user@remote-hostThen connect to the tunneled Docker:
DOCKER_HOST=tcp://localhost:2375 npm startVS Code Integration
To integrate with VS Code and GitHub Copilot, add the following to your MCP settings file:
For Local Docker (~/.vscode/mcp-settings.json or in your workspace settings):
{
"mcpServers": {
"docker": {
"command": "node",
"args": ["/path/to/Docker-MCP/dist/index.js"]
}
}
}For Remote Docker over TCP:
{
"mcpServers": {
"docker": {
"command": "node",
"args": ["/path/to/Docker-MCP/dist/index.js"],
"env": {
"DOCKER_HOST": "tcp://192.168.1.100:2375"
}
}
}
}For Remote Docker with TLS:
{
"mcpServers": {
"docker": {
"command": "node",
"args": ["/path/to/Docker-MCP/dist/index.js"],
"env": {
"DOCKER_HOST": "https://192.168.1.100:2376",
"DOCKER_TLS_VERIFY": "1",
"DOCKER_CERT_PATH": "/home/user/.docker/certs"
}
}
}
}For Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):
{
"mcpServers": {
"docker": {
"command": "node",
"args": ["/path/to/Docker-MCP/dist/index.js"]
}
}
}Alternative using npx (after publishing to npm):
{
"mcpServers": {
"docker": {
"command": "npx",
"args": ["@swartdraak/docker-mcp-server"]
}
}
}Testing Your Connection
Connection Test Utility
Before using the MCP server, you can test your Docker connection with the included test utility:
# Test local Docker connection
node test-connection.js
# Test remote Docker via TCP
DOCKER_HOST=tcp://192.168.1.100:2375 node test-connection.js
# Test remote Docker via TLS
DOCKER_HOST=https://192.168.1.100:2376 \
DOCKER_TLS_VERIFY=1 \
DOCKER_CERT_PATH=~/.docker/certs \
node test-connection.js
# Test SSH tunnel connection
DOCKER_HOST=tcp://localhost:2375 node test-connection.jsThe test utility will:
โ Validate your configuration
โ Check TLS certificates (if applicable)
โ Test connection to Docker daemon
โ Verify Docker operations (list containers, images, networks, volumes)
โ Display system information
โ Provide troubleshooting recommendations
validate_connection Tool
Once the MCP server is running, you can also use the validate_connection tool from your AI assistant:
"Validate my Docker connection"This tool performs runtime connectivity tests and returns:
Connection status
Docker version and API information
System information
Test results for common operations
Manual Testing
You can also test your connection directly with Docker CLI:
# Set environment variables
export DOCKER_HOST=tcp://192.168.1.100:2375
# Test commands
docker version
docker info
docker psFor detailed setup instructions, troubleshooting, and platform-specific guides, see:
CONFIGURATION.md - MCP server configuration examples
REMOTE_SETUP.md - Comprehensive remote connection setup guide (includes Windows 11 specific instructions)
Available Tools (37 Total)
Container Operations (15 tools)
list_containers
List all Docker containers (running or all)
{
"all": true
}create_container
Create a new Docker container
{
"image": "nginx:latest",
"name": "my-nginx",
"command": ["nginx", "-g", "daemon off;"],
"env": ["NODE_ENV=production"],
"exposedPorts": {"80/tcp": {}},
"hostConfig": {
"PortBindings": {"80/tcp": [{"HostPort": "8080"}]},
"Binds": ["/host/path:/container/path"]
}
}run_container
Create and start a container (recommended)
{
"image": "python:3.9",
"name": "my-python-app",
"command": ["python", "app.py"],
"env": ["DEBUG=true", "PORT=5000"]
}start_container
Start a stopped container
{
"containerId": "container_id_or_name"
}stop_container
Stop a running container
{
"containerId": "container_id_or_name",
"timeout": 10
}remove_container
Remove a container
{
"containerId": "container_id_or_name",
"force": false,
"volumes": false
}inspect_container
Get detailed container information
{
"containerId": "container_id_or_name"
}container_logs
Get container logs
{
"containerId": "container_id_or_name",
"tail": 100,
"follow": false
}exec_container ๐
Execute a command in a running container
{
"containerId": "container_id_or_name",
"command": ["ls", "-la", "/app"],
"workingDir": "/app",
"env": ["DEBUG=true"]
}container_stats ๐
Get real-time resource usage statistics (CPU, memory, network, I/O)
{
"containerId": "container_id_or_name",
"stream": false
}restart_container ๐
Restart a Docker container
{
"containerId": "container_id_or_name",
"timeout": 10
}pause_container ๐
Pause all processes within a container
{
"containerId": "container_id_or_name"
}unpause_container ๐
Unpause all processes within a container
{
"containerId": "container_id_or_name"
}rename_container ๐
Rename a Docker container
{
"containerId": "container_id_or_name",
"newName": "new-container-name"
}prune_containers ๐
Remove all stopped containers
{}Image Operations (7 tools)
list_images
List Docker images
{
"all": false
}pull_image
Pull an image from registry
{
"image": "nginx:latest"
}build_image ๐
Build a Docker image from a Dockerfile
{
"context": "/path/to/build/context",
"dockerfile": "Dockerfile",
"tag": "myimage:latest",
"buildArgs": {
"NODE_VERSION": "18"
}
}tag_image ๐
Tag an image with a new name/tag
{
"image": "myimage:latest",
"repo": "myrepo/myimage",
"tag": "v1.0.0"
}push_image ๐
Push an image to a Docker registry
{
"image": "myrepo/myimage:v1.0.0"
}remove_image ๐
Remove a Docker image
{
"image": "image_id_or_name",
"force": false
}prune_images ๐
Remove unused images
{
"all": false
}Network Operations (7 tools)
list_networks
List Docker networks
{}create_network ๐
Create a Docker network
{
"name": "my-network",
"driver": "bridge",
"internal": false
}inspect_network ๐
Get detailed information about a network
{
"networkId": "network_id_or_name"
}connect_network ๐
Connect a container to a network
{
"networkId": "network_id_or_name",
"containerId": "container_id_or_name"
}disconnect_network ๐
Disconnect a container from a network
{
"networkId": "network_id_or_name",
"containerId": "container_id_or_name",
"force": false
}remove_network ๐
Remove a Docker network
{
"networkId": "network_id_or_name"
}prune_networks ๐
Remove all unused networks
{}Volume Operations (5 tools)
list_volumes
List Docker volumes
{}create_volume ๐
Create a Docker volume
{
"name": "my-volume",
"driver": "local",
"labels": {
"environment": "production"
}
}inspect_volume ๐
Get detailed information about a volume
{
"volumeName": "volume_name"
}remove_volume ๐
Remove a Docker volume
{
"volumeName": "volume_name",
"force": false
}prune_volumes ๐
Remove all unused volumes
{}System Operations (3 tools)
system_info ๐
Get Docker system information
{}system_version ๐
Get Docker version information
{}validate_connection ๐
Validate Docker connection and test basic operations. Returns connection status, configuration details, and test results. Useful for troubleshooting connection issues.
{}Key Features: Array Handling
This MCP server correctly handles arrays for:
Command: Passed as an array of strings
["python", "app.py", "--port", "8000"]Entrypoint: Passed as an array of strings
["/bin/bash", "-c"]Environment Variables: Passed as an array of
KEY=VALUEstrings["NODE_ENV=production", "PORT=3000"]Volume Bindings: Passed as an array of bind strings
["/host/path:/container/path"]
This resolves the common "array issue" where MCP servers incorrectly expect strings instead of arrays, causing errors when integrated with VS Code and GitHub Copilot.
Development
Scripts
npm run build- Compile TypeScript to JavaScriptnpm run watch- Watch mode for developmentnpm start- Run the compiled servernpm run dev- Build and run
Project Structure
Docker-MCP/
โโโ src/
โ โโโ index.ts # Main server implementation
โโโ dist/ # Compiled JavaScript output
โโโ package.json # Project dependencies
โโโ tsconfig.json # TypeScript configuration
โโโ README.md # This fileTroubleshooting
Docker Connection Issues
If you get "Cannot connect to Docker daemon" errors:
Ensure Docker is running:
docker psCheck Docker socket permissions
On Linux: Add your user to the docker group:
sudo usermod -aG docker $USER
Remote Docker Connection Issues
If you can't connect to a remote Docker host:
TCP Connection Issues:
Ensure the Docker daemon is configured to listen on TCP: Check
/etc/docker/daemon.jsonVerify the port is open:
telnet remote-host 2375Check firewall rules on the remote host
Ensure
DOCKER_HOSTenvironment variable is set correctly
TLS/HTTPS Connection Issues:
Verify certificates are in the correct directory
Check certificate file names:
ca.pem,cert.pem,key.pemEnsure certificates are readable:
chmod 644 ca.pem cert.pem key.pemVerify
DOCKER_TLS_VERIFY=1andDOCKER_CERT_PATHare setTest with Docker CLI first:
docker --tlsverify --host=tcp://remote-host:2376 ps
SSH Tunnel Issues:
Verify SSH tunnel is running:
ps aux | grep sshTest tunnel:
curl http://localhost:2375/versionEnsure local port is not already in use:
lsof -i :2375Try reconnecting the tunnel if connection is lost
VS Code Integration Issues
If the MCP server doesn't appear in VS Code:
Verify the path to
dist/index.jsis absoluteCheck that the server builds successfully:
npm run buildRestart VS Code after updating MCP settings
Check VS Code Output panel for MCP-related errors
Verify environment variables in MCP settings are correct
Array-Related Errors
This server is specifically designed to handle arrays correctly. If you encounter array errors:
Ensure you're passing arrays for
command,entrypoint, andenvfieldsVerify JSON formatting in tool arguments
Check that arrays contain string items
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
Development Workflow
This project follows industry-standard practices:
Branching Strategy: Git Flow (see BRANCHING.md)
Versioning: Semantic Versioning 2.0.0 (see VERSIONING.md)
Commit Convention: Conventional Commits
CI/CD: Automated testing and releases via GitHub Actions
Quick Start for Contributors
Fork the repository
Create a feature branch:
git checkout -b feature/my-featureMake your changes with conventional commits
Ensure tests pass:
npm testPush and create a Pull Request to
develop
For detailed guidelines, see CONTRIBUTING.md.
Branch Structure
main- Production releases (protected)develop- Integration branch (protected)feature/*- New featuresbugfix/*- Bug fixeshotfix/*- Critical production fixes
Release Process
Releases are automated via GitHub Actions when tags are pushed:
# Create release branch
git checkout -b release/2.1.0
# Update version and changelog
npm version minor
# Merge to main and tag
git checkout main
git merge --no-ff release/2.1.0
git tag -a v2.1.0 -m "Version 2.1.0"
git push origin main --tagsSee VERSIONING.md for complete release procedures.
For detailed usage examples, see EXAMPLES.md.
For configuration help, see CONFIGURATION.md.
Documentation
User Documentation
README.md - Main documentation and quick start
CONFIGURATION.md - Configuration options and setup
EXAMPLES.md - Usage examples for all tools
REMOTE_SETUP.md - Remote Docker connection setup
MIGRATION.md - Migration guides for version upgrades
Developer Documentation
CONTRIBUTING.md - Contribution guidelines and workflow
BRANCHING.md - Git Flow branching strategy
VERSIONING.md - Semantic versioning and release process
RELEASE_GUIDE.md - npm package release procedures
BRANCH_PROTECTION.md - Branch protection setup guide
CHANGELOG.md - Version history and changes
SECURITY.md - Security policies and reporting
License
MIT License - see LICENSE file for details
Author
Swartdraak (eternusprocer@gmail.com)
Available Tools
12 toolscontainer_logsC
Get logs from a container
| Name | Required | Description | Default |
|---|---|---|---|
| containerId | Yes | Container ID or name | |
| tail | No | Number of lines to show from the end of the logs | |
| follow | No | Follow log output |
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. 'Get logs' implies a read-only operation, but it doesn't specify critical behaviors such as whether this requires specific permissions, if it streams logs in real-time, potential rate limits, or what happens if the container doesn't exist. The description is too minimal for a tool with potential operational impact.
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 zero wasted words. It's front-loaded with the core purpose and appropriately sized for a straightforward tool, making it easy for an agent 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 the complexity of container operations and the lack of annotations and output schema, the description is insufficient. It doesn't cover what the logs contain, how they're formatted, error conditions, or behavioral nuances like streaming. For a tool that interacts with system resources, this leaves significant gaps in understanding.
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 already documents all parameters (containerId, tail, follow) with clear descriptions. The description adds no additional meaning beyond what's in the schema, such as explaining log format or interaction between parameters. 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Get logs from a container' clearly states the verb ('Get') and resource ('logs from a container'), making the tool's purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'inspect_container' or specify what type of logs (e.g., stdout/stderr, application vs. system), which prevents a perfect score.
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. It doesn't mention prerequisites (e.g., the container must be running or exist), nor does it differentiate from sibling tools like 'inspect_container' that might provide related information. This lack of contextual guidance leaves the agent to infer usage scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
create_containerC
Create a new Docker container
| Name | Required | Description | Default |
|---|---|---|---|
| image | Yes | Docker image name (e.g., 'nginx:latest') | |
| name | No | Container name | |
| command | No | Command to run as an array (e.g., ['python', 'app.py']) | |
| entrypoint | No | Entrypoint as an array (e.g., ['/bin/bash', '-c']) | |
| env | No | Environment variables as array of KEY=VALUE strings (e.g., ['NODE_ENV=production', 'PORT=3000']) | |
| exposedPorts | No | Exposed ports as object with port/protocol keys (e.g., {'80/tcp': {}, '443/tcp': {}}) | |
| hostConfig | No | Host configuration including port bindings and volumes | |
| labels | No | Container labels as key-value pairs |
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 but offers minimal information. It states this is a creation operation but doesn't mention whether it requires specific permissions, what happens if a container with the same name exists, whether the container starts automatically, or what the typical response format might be. For a complex mutation tool with 8 parameters, this is inadequate behavioral context.
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 at just 4 words, front-loading the essential purpose without any wasted words. While this brevity comes at the cost of completeness, as a standalone statement it's efficiently structured.
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?
For a complex container creation tool with 8 parameters, no annotations, no output schema, and multiple sibling tools, the description is severely incomplete. It doesn't address behavioral aspects, usage context, or provide any guidance beyond the basic purpose, leaving significant gaps for an agent trying to use this tool effectively.
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 description adds no parameter information beyond what's already in the schema, which has 100% coverage with detailed descriptions for all 8 parameters. The baseline score of 3 reflects that the schema does the heavy lifting, though the description could have provided higher-level context about how parameters relate or typical usage patterns.
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 'Create a new Docker container' clearly states the verb ('create') and resource ('Docker container'), making the purpose immediately understandable. However, it doesn't differentiate this tool from its sibling 'run_container', which appears to serve a similar function, leaving some ambiguity about when to use one versus the other.
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 'run_container' or other container management tools. There's no mention of prerequisites, typical use cases, or constraints that would help an agent select this tool appropriately from among the 11 sibling tools available.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
inspect_containerC
Get detailed information about a container
| Name | Required | Description | Default |
|---|---|---|---|
| containerId | Yes | Container ID or name |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states this is a 'Get' operation, implying read-only behavior, but doesn't confirm if it's safe, whether it requires specific permissions, what format the detailed information returns, or if there are rate limits. The description adds minimal behavioral context beyond the basic purpose.
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 zero wasted words. It's appropriately sized for a simple tool and front-loads the core purpose immediately.
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 for a tool that presumably returns detailed container information. It doesn't explain what 'detailed information' includes (e.g., status, configuration, resources) or the response format, leaving significant gaps for the agent to understand the tool's behavior and output.
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 100% description coverage, with the single parameter 'containerId' documented as 'Container ID or name'. The description adds no additional parameter semantics beyond what's in the schema, so it meets the baseline of 3 where the schema does the heavy lifting.
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 ('Get') and resource ('detailed information about a container'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'list_containers' or 'container_logs' which also provide information about containers, missing an opportunity for clearer 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. It doesn't specify that this is for detailed metadata rather than logs (container_logs) or a summary list (list_containers), nor does it mention prerequisites like needing a container ID.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_containersC
List all Docker containers
| Name | Required | Description | Default |
|---|---|---|---|
| all | No | Show all containers (default shows just running) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states 'List all Docker containers' but doesn't disclose behavioral traits such as output format, pagination, permissions required, rate limits, or whether it's a read-only operation. 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's front-loaded with the core 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 doesn't address behavioral aspects like output format or operational context, which are crucial for a tool that interacts with system resources like Docker containers. More detail is needed to compensate for the missing structured data.
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 100% description coverage, with the 'all' parameter fully documented in the schema. The description adds no additional parameter semantics beyond what the schema provides, so the baseline score of 3 is appropriate as the schema does the heavy lifting.
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 ('List') and resource ('all Docker containers'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'list_images' or 'list_networks' beyond the resource name, missing explicit 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. It doesn't mention prerequisites, context for listing containers, or compare to related tools like 'inspect_container' for detailed views, leaving usage decisions to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_imagesC
List Docker images
| Name | Required | Description | Default |
|---|---|---|---|
| all | No | Show all images (default hides intermediate) |
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. 'List Docker images' implies a read-only operation, but it doesn't specify whether this requires specific permissions, how results are formatted (e.g., JSON, table), if there's pagination, or what happens on errors. For a tool with zero annotation coverage, this leaves critical behavioral traits undocumented.
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 at just three words ('List Docker images'), with zero wasted language. It's front-loaded and directly states the core functionality without unnecessary elaboration, 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.
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 for effective tool use. It doesn't explain what the tool returns (e.g., image IDs, tags, sizes), how results are structured, or any behavioral nuances. For a listing tool in a Docker context with multiple similar siblings, more context is needed to distinguish it and guide proper invocation.
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 100% description coverage, with the single parameter 'all' fully documented in the schema itself ('Show all images (default hides intermediate)'). The description adds no additional parameter information beyond what the schema provides, so it meets the baseline score of 3 for high schema coverage without compensating value.
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 Docker images' clearly states the verb ('List') and resource ('Docker images'), making the tool's purpose immediately understandable. However, it doesn't differentiate this from sibling tools like 'list_containers', 'list_networks', or 'list_volumes', which all follow the same 'list [resource]' pattern without specifying what distinguishes listing images from listing other Docker resources.
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. It doesn't mention prerequisites (e.g., Docker daemon running), compare it to similar tools like 'inspect_container' for detailed views, or explain why one might list images instead of containers. With multiple sibling listing tools, this lack of differentiation leaves usage context unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_networksB
List Docker networks
| 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. 'List Docker networks' implies a read-only operation, but it doesn't specify what information is returned (e.g., network names, IDs, types), whether there are rate limits, authentication requirements, or how results are formatted (e.g., JSON, plain text). This leaves significant gaps for an agent to understand the tool's behavior.
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 at just three words ('List Docker networks'), which is front-loaded and wastes no space. For a simple tool with no parameters, this is appropriately sized and efficient.
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 low complexity (0 parameters, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks details on behavior, usage context, or output format. Without annotations or output schema, the agent must infer these aspects, making this description incomplete for optimal use.
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 tool has 0 parameters, and the input schema has 100% description coverage (though empty). The description doesn't need to add parameter semantics, so it meets the baseline expectation. No points are deducted since there are no parameters to document.
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 Docker networks' clearly states the verb ('List') and resource ('Docker networks'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'list_containers' or 'list_images' beyond the resource name, which keeps it from a perfect score.
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. There are multiple list-related siblings (list_containers, list_images, list_volumes), but the description doesn't explain when to choose networks over those or mention any prerequisites or context for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_volumesB
List Docker volumes
| 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 but offers minimal information. It implies a read-only operation (listing) but doesn't cover critical aspects like permissions required, rate limits, output format (e.g., JSON list), or error conditions (e.g., Docker not installed). This is inadequate 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 extremely concise ('List Docker volumes')โa single phrase with no wasted words. It's front-loaded with the core action and resource, making it easy to parse. This efficiency is ideal for a simple listing tool.
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 simplicity (0 parameters, no output schema, no annotations), the description is minimally complete. It identifies the resource but lacks context about behavior, output, or integration with siblings. For a basic read operation, it's adequate but leaves gaps in usage and transparency that could hinder an 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?
The tool has 0 parameters with 100% schema description coverage (empty schema), so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate, but it also doesn't imply any hidden parameters or constraints. Baseline 4 is given for zero-parameter tools where the schema fully covers the absence of inputs.
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 Docker volumes' clearly states the verb ('List') and resource ('Docker volumes'), making the purpose immediately understandable. It distinguishes from siblings like 'list_containers' and 'list_images' by specifying volumes. However, it lacks specificity about scope (e.g., all volumes vs. filtered), preventing a perfect score.
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. It doesn't mention prerequisites (e.g., Docker daemon running), compare to other listing tools (e.g., 'list_containers'), or specify use cases (e.g., checking available storage). This leaves the agent with minimal context for decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
pull_imageC
Pull a Docker image from a registry
| Name | Required | Description | Default |
|---|---|---|---|
| image | Yes | Image name with optional tag (e.g., 'nginx:latest') |
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. While 'pull' implies a network operation that fetches data, the description doesn't mention authentication needs (e.g., for private registries), rate limits, whether it overwrites existing images, what happens on failure, or the expected output format. This leaves significant gaps for a tool that interacts with external registries.
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 purpose without unnecessary words. It's front-loaded with the core action and resource, making it easy to parse quickly. Every word earns its place in conveying the essential function.
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 Docker operations (network-dependent, potential authentication, side effects) and the lack of annotations and output schema, the description is insufficient. It doesn't explain what 'pull' entails behaviorally (e.g., downloads layers, updates local cache), success/failure conditions, or output details, leaving the agent with incomplete context for reliable use.
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%, with the single parameter 'image' well-documented in the schema as 'Image name with optional tag (e.g., 'nginx:latest')'. The description adds no additional parameter information beyond what the schema provides, so it meets the baseline for high schema coverage without compensating value.
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 ('pull') and resource ('Docker image from a registry'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'list_images' or 'run_container' which might also involve Docker images, leaving some ambiguity about when this specific pull operation is needed versus other image-related operations.
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. With siblings like 'list_images' (to check existing images), 'run_container' (which might pull images automatically), and 'create_container' (which might require images), there's no indication of prerequisites, typical workflows, or when pulling is necessary versus optional.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
remove_containerC
Remove a Docker container
| Name | Required | Description | Default |
|---|---|---|---|
| containerId | Yes | Container ID or name | |
| force | No | Force removal of running container | |
| volumes | No | Remove associated volumes |
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. 'Remove' implies a destructive operation, but the description doesn't clarify that this permanently deletes the container, what happens to running containers (hinted at by force parameter but not explained), or whether removal is irreversible. For a destructive tool with zero annotation coverage, this is inadequate behavioral 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 directly states the tool's function without any unnecessary words. It's perfectly front-loaded and every word earns its place. This is an excellent example of conciseness for a simple tool.
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 this is a destructive operation with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what 'remove' entails (permanent deletion), what happens to associated resources, potential side effects, or what the tool returns. For a 3-parameter tool that performs irreversible changes, more contextual information is needed.
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 already fully documents all three parameters (containerId, force, volumes) with their types, descriptions, and defaults. The description adds no additional parameter information beyond what's in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description.
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 'Remove a Docker container' clearly states the action (remove) and resource (Docker container), making the purpose immediately understandable. It distinguishes from siblings like stop_container (which stops but doesn't remove) and list_containers (which only lists). However, it doesn't explicitly mention that this is a destructive deletion operation versus other container management actions.
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. It doesn't mention prerequisites (e.g., container must be stopped unless force=true), when to choose remove_container over stop_container, or what happens to associated resources. With multiple sibling tools for container management, this lack of comparative guidance is a significant gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
run_containerB
Run a container (create and start). This is the preferred method for starting containers.
| Name | Required | Description | Default |
|---|---|---|---|
| image | Yes | Docker image name (e.g., 'nginx:latest') | |
| name | No | Container name | |
| command | No | Command to run as an array (e.g., ['python', 'app.py']) | |
| entrypoint | No | Entrypoint as an array (e.g., ['/bin/bash', '-c']) | |
| env | No | Environment variables as array of KEY=VALUE strings (e.g., ['NODE_ENV=production', 'PORT=3000']) | |
| exposedPorts | No | Exposed ports as object with port/protocol keys (e.g., {'80/tcp': {}, '443/tcp': {}}) | |
| hostConfig | No | Host configuration including port bindings and volumes | |
| labels | No | Container labels as key-value pairs |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. While it mentions the tool creates and starts containers, it lacks critical information about permissions needed, whether this is a destructive operation (e.g., if a container with the same name exists), resource implications, error handling, or what happens on failure. For a complex container management tool, this is a significant gap.
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 just two sentences that are front-loaded and zero waste. Every word earns its place by stating the action and providing usage guidance 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 (8 parameters with nested objects), no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what the tool returns, error conditions, side effects, or behavioral nuances needed for a container execution tool. The description should provide more context about the operation's implications.
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 already documents all 8 parameters thoroughly. The description adds no additional parameter information beyond what's in the schema, so it meets the baseline of 3 where the schema does the heavy lifting without compensating for any 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 action ('run a container') and specifies it involves both creation and starting, which distinguishes it from siblings like 'create_container' and 'start_container'. However, it doesn't explicitly contrast with these specific alternatives, missing full sibling differentiation.
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 clear context by stating this is the 'preferred method for starting containers', which implies when to use it over alternatives like 'create_container' followed by 'start_container'. However, it doesn't explicitly mention when NOT to use it or detail specific scenarios where other tools might be better.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
start_containerB
Start a stopped Docker container
| Name | Required | Description | Default |
|---|---|---|---|
| containerId | Yes | Container ID or name |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. While 'Start' implies a state-changing operation, it doesn't describe permissions needed, whether this affects container data, error conditions (e.g., if container doesn't exist), or what happens on success. For a mutation tool with zero annotation coverage, this is insufficient.
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 zero wasted words. It's front-loaded with the core purpose and uses precise technical language appropriate for Docker operations.
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?
For a mutation tool with no annotations and no output schema, the description is incomplete. It doesn't explain what happens after starting (does it return container status? success confirmation?), error conditions, or behavioral nuances. Given the complexity of container operations and lack of structured metadata, more context is needed.
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%, with the single parameter 'containerId' well-documented in the schema as 'Container ID or name'. The description doesn't add any parameter details beyond what the schema provides, so 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Start') and resource ('a stopped Docker container'), using precise technical terminology. It distinguishes from siblings like 'stop_container', 'run_container', and 'create_container' by focusing exclusively on restarting already-existing containers.
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 context by specifying 'a stopped Docker container', suggesting it should only be used on containers that are currently stopped. However, it doesn't explicitly state when NOT to use it (e.g., for running containers or creating new ones) or name specific alternatives like 'run_container' for starting new containers.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
stop_containerC
Stop a running Docker container
| Name | Required | Description | Default |
|---|---|---|---|
| containerId | Yes | Container ID or name | |
| timeout | No | Seconds to wait before killing the container |
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 ('Stop') but doesn't clarify critical details: whether this is a graceful stop versus force kill, what happens to container resources after stopping, if it requires specific permissions, or what the response looks like (e.g., success/failure indicators). This is inadequate for a mutation 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, direct sentence with zero wasted words. It front-loads the core action and target, making it highly efficient and easy to parse. Every word earns its place by conveying essential purpose without redundancy.
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 a mutation tool (stopping a container) with no annotations and no output schema, the description is incomplete. It lacks behavioral details (e.g., side effects, error handling) and usage context, which are crucial for an agent to invoke this tool correctly and safely. The high schema coverage doesn't compensate for these gaps in operational guidance.
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 description adds no parameter-specific information beyond what the input schema already provides. Since schema description coverage is 100%, with clear documentation for both 'containerId' and 'timeout', the baseline score of 3 is appropriate. The description doesn't compensate with additional context like format examples or usage tips.
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 ('Stop') and target ('a running Docker container'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'remove_container' or 'start_container', but the verb 'Stop' is specific enough to imply halting execution rather than deletion or initiation.
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. It doesn't mention prerequisites (e.g., the container must be running), exclusions (e.g., not for stopped containers), or comparisons to siblings like 'remove_container' for deletion or 'start_container' for restarting. This leaves the agent to infer usage from context 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. Dates show when Glama detected each change.
12 tool updates
v1.0.0- First observed
container_logs - First observed
create_container - First observed
inspect_container - First observed
list_containers - First observed
list_images - First observed
list_networks - First observed
list_volumes - First observed
pull_image - First observed
remove_container - First observed
run_container - First observed
start_container - First observed
stop_container
TDQS
Every tool has a clearly distinct purpose targeting specific Docker resources and actions. For example, create_container and run_container are well-differentiated by their descriptions, with run_container explicitly noted as the preferred method for starting containers. There is no ambiguity or overlap in functionality across the tool set.
All tool names follow a consistent verb_noun pattern using snake_case, such as list_containers, create_container, and stop_container. This predictable naming convention makes it easy for agents to understand and select the appropriate tools without confusion.
With 12 tools, the server is well-scoped for managing Docker containers, images, networks, and volumes. Each tool earns its place by covering essential operations like creation, listing, starting, stopping, and removal, without being overly sparse or bloated.
The tool set provides strong coverage for core Docker operations, including CRUD for containers and listing for other resources. Minor gaps exist, such as no tools for creating or removing networks/volumes, but agents can still perform most workflows effectively with the available tools.
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