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compose_up

Start a Docker Compose project in detached mode, with options to build, pull images, select services or profiles, and wait for healthy services.

Instructions

Bring up a Docker Compose project, detached.

Always runs detached (-d) so it can't block the server. Use compose_ps to confirm services are running, or wait=True to block until they're healthy.

args: project_dir - Dir with the compose file (default: server cwd; paths verbatim, no shell expansion) files - Explicit compose file paths (repeatable, -f) project_name - Compose project name override profiles - Profiles to activate services - Specific services to bring up (default: all) build - Build images before starting pull - Pull strategy: "always", "missing", "never", or "policy" (compose default) remove_orphans - Remove containers for services not in the compose file wait - Block until services are healthy (adds --wait) timeout_seconds - Subprocess timeout (default 600s) returns: dict - {"returncode": int, "stdout": str, "stderr": str, "truncated": bool}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pullNo
waitNo
buildNo
filesNo
profilesNo
servicesNo
project_dirNo
project_nameNo
remove_orphansNo
timeout_secondsNo
Behavior5/5

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

Description goes beyond annotations by detailing that it always runs detached, cannot block the server, includes a timeout, and returns a specific dict. Annotations (readOnlyHint=false, destructiveHint=false) are consistent and the description adds significant 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.

Conciseness4/5

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

The description is well-structured with a brief intro, a key behavioral note, and a parameter list. It is front-loaded but slightly lengthy due to verbose parameter explanations; still understandable and focused.

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?

For a tool with 10 parameters and no output schema, the description covers return value structure and parameter usage well. However, it omits prerequisites (e.g., existing Compose file) and explicit confirmation of required Docker context. Annotations are minimal but adequate.

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?

Schema has 0% description coverage, but the description provides detailed explanations for all 10 parameters, including defaults, allowed values (e.g., pull strategy), and behavioral notes like 'paths verbatim, no shell expansion'. This fully compensates for the missing schema descriptions.

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 'Bring up a Docker Compose project, detached,' using a specific verb+resource. It distinguishes from sibling tools like compose_start, compose_run, etc., as the primary command for starting a Compose project.

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?

Description explains that the tool always runs detached and cannot block, and suggests using compose_ps for verification or wait=True for blocking. It provides guidance on when to use alternatives implicitly, though lacks explicit 'when not to use' statements.

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

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