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Create Application — Docker Image

coolify_create_application_dockerimage

Deploy a Docker image from any registry to a Coolify server. Specify the image name, tag, ports, and domain to create the application in the target environment.

Instructions

Create an application from any Docker registry without Git. This is the standard pattern for Devon's Flavor B/C apps: GitHub Actions builds → pushes to ghcr.io/alobarquest/ → Coolify pulls.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoApplication display name
revealNoReveal redacted secret values in the response (default false; the call is audited)
domainsNoFQDN for the app
instanceYesREQUIRED — which Coolify instance to mutate: 'prod' (Hetzner VPS) or 'dev' (local OrbStack VM). No default: state the target explicitly so a write never lands on prod by accident.
descriptionNoApplication description
server_uuidYesUUID of the destination server
project_uuidYesUUID of the target project
ports_exposesNoPorts to expose (default: 8000)8000
destination_uuidYesUUID of the Docker network/destination
environment_nameNoEnvironment nameproduction
docker_registry_image_tagNoImage tag (default: latest)latest
docker_registry_image_nameYesFull image name (e.g. ghcr.io/alobarquest/myapp)
Behavior4/5

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

Annotations already mark this as mutable and non-destructive. Description adds valuable context: the 'instance' parameter is REQUIRED to prevent accidental prod writes, and explains the standard workflow. No contradiction with annotations.

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 in the description, plus a well-placed safety note in the instance parameter. Every sentence adds value without redundancy. Front-loaded with purpose and context.

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?

Covers the creation scenario well given schema completeness. Lacks details on post-creation behavior (e.g., automatic deployment or response format), but annotations do not require it. Adequate for a tool with 12 parameters and no output schema.

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?

Schema coverage is 100%, so baseline is 3. Description adds meaning for key parameters: 'instance' explains its role as a safety measure with enumeration, and 'docker_registry_image_name' includes an example. Other parameters rely on schema descriptions, which are adequate.

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?

Clearly states 'Create an application from any Docker registry without Git', specifying the verb and resource. Distinguishes from sibling creation tools that use other sources (deploykey, dockercompose, dockerfile, githubapp, public). Includes a concrete workflow pattern for Devon's Flavor B/C apps.

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?

Describes the typical use case (apps built via GitHub Actions pushing to a registry). Implicitly differentiates from Git-based creation methods. Lacks explicit when-not-to-use statements but provides sufficient context for an AI to select this tool over siblings.

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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