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aidenappl
by aidenappl

lattice_create_container

Add a container definition to a stack by specifying name, image, and tag. Configure optional settings like volumes, ports, and environment variables. Deploy the stack to start the container.

Instructions

Add a container definition to a stack. Creates the record only — deploy the stack to actually start it. JSON-shaped fields are passed as strings, matching the API

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagYesImage tag, e.g. 'latest'
nameYesContainer name
imageYesImage, e.g. registry.appleby.cloud/openbucket-api
commandNoOverride the image command
volumesNoJSON object string of host:container volume mappings
env_varsNoJSON object string of environment variables
replicasNoReplica count
stack_idYesStack to add the container to
cpu_limitNoCPU limit in cores
entrypointNoOverride the image entrypoint
health_checkNoJSON object string describing the healthcheck
memory_limitNoMemory limit in bytes
port_mappingsNoJSON array string, e.g. '[{"container_port":"8000","host_port":"8080","protocol":"tcp"}]'
restart_policyNoe.g. 'unless-stopped', 'always'
Behavior3/5

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

With no annotations, the description carries full burden. It discloses the record-only behavior and the string input requirement for JSON fields, but omits other behavioral details like authorization needs, error handling, or idempotency. This is adequate but leaves room for improvement.

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 sentences with zero wasted words. The first sentence states the primary action, the second clarifies the behavioral scope and data format. Front-loaded and efficient.

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?

Given 14 parameters fully described in the schema, the description covers the critical behavioral context (record vs. deploy, string format for JSON fields). It lacks output description, but since no output schema exists, this is a minor gap for a creation tool that likely returns the new container ID.

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. The description adds value by explaining that JSON-shaped fields (volumes, env_vars, port_mappings, health_check) are passed as strings, which is not obvious from the parameter descriptions alone. This elevates the score.

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 action 'Add a container definition to a stack' and distinguishes from actual deployment with 'Creates the record only — deploy the stack to actually start it.' This verb+resource combination is specific and differentiates from sibling tools like lattice_deploy_stack.

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?

The description explicitly tells when to use this tool (to create the record) and when not (to start it, use deploy). It also provides a practical tip about JSON-shaped fields being passed as strings. While it doesn't enumerate alternatives, the deployment distinction is sufficient for most use cases.

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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