Skip to main content
Glama

compose_stop

Stop Docker Compose services without removing containers, networks, or volumes, so you can restart them later with compose_start.

Instructions

Stop services in a compose project without removing their containers.

Unlike compose_down, containers/networks/volumes survive — use compose_start to bring them back.

args: project_dir - Dir with the compose file (default: server cwd; copied to the target host if no local plugin) files - Explicit compose file paths (repeatable, -f) project_name - Compose project name override services - Specific services to stop (default: all) stop_timeout_seconds - Grace period before SIGKILL (passed as --timeout) timeout_seconds - Subprocess timeout (default 300s) returns: dict - {"returncode": int, "stdout": str, "stderr": str, "truncated": bool}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filesNo
servicesNo
project_dirNo
project_nameNo
timeout_secondsNo
stop_timeout_secondsNo
Behavior5/5

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

Although annotations declare readOnlyHint=false and destructiveHint=false, the description adds crucial context: containers/networks/volumes survive, stop_timeout_seconds is passed as --timeout, and return format is specified. It clarifies the non-destructive nature and subprocess timeout behavior, going beyond annotation bare minimum.

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: a concise summary, contrast sentence, parameter list, and return spec. Every line adds value with no fluff, and the most important information is front-loaded.

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

Completeness5/5

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

Given the tool's moderate complexity, the description covers purpose, distinctions, parameters, defaults, return type, and key behaviors like timeout handling. It is self-contained and leaves no significant gaps for an agent to invoke it correctly.

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

Parameters5/5

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

With 0% schema description coverage, the description fully compensates by explaining every parameter in the args block, including defaults and nuances like project_dir being copied to target host. Each parameter gets a clear, human-readable meaning.

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 tool stops services without removing containers, using a specific verb and resource. It explicitly distinguishes itself from compose_down, making its purpose unambiguous relative to sibling tools.

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 directly compares with compose_down and recommends compose_start to bring services back, providing explicit when-to-use and when-not-to-use guidance. This is a model of clear alternative selection.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/L337-org/docker-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server