Skip to main content
Glama

container_create

Create a container from an image without starting it, so you can apply configuration before the first launch. Use this when you need to separate creation from startup and start the container later.

Instructions

Create a container from an image without starting it.

Use this when you need to configure a container (with extra_kwargs) before its first start, or want creation and start as separate observable steps. For the common case of create-then-start-immediately use container_run instead — it does both in one call. Start the created container with container_start. Common extra_kwargs keys: name (str), environment (list of "KEY=VAL" or dict), ports (dict, e.g. {"80/tcp": 8080}), volumes (dict, e.g. {"/host/path": {"bind": "/container/path", "mode": "rw"}}), labels (dict). For anything else docker-py's ContainerCollection.create accepts, call docs_lookup(section="containers") rather than guessing a key name.

args: image - Image to create the container from, e.g. "nginx:alpine" command - Override the image's default command; string or list of strings extra_kwargs - Additional docker-py ContainerCollection.create keyword arguments returns: dict - The created container's attrs (not yet running)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
imageYes
commandNo
extra_kwargsNo
Behavior4/5

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

Annotations already indicate this is not read-only and not destructive, and the description aligns by stating it creates a container. It adds key behavioral context: the container is not started, and the return value is 'the created container's attrs (not yet running).' It does not describe lifecycle persistence or cleanup implications, but the annotation coverage plus explicit creation/not-running behavior make this sufficient.

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 one-sentence purpose, a usage paragraph, an args section, and a returns line. Every sentence adds value—examples and alternatives are concrete rather than filler. Though slightly long, the density of actionable information justifies the length.

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?

For a 3-parameter tool with no output schema, the description covers purpose, usage context, alternatives, parameter details, common kwargs, and return type. It also explains when not to use it (common case should use container_run) and how to discover additional options. This is complete enough for an agent to invoke correctly without prior knowledge.

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 description coverage is 0%, so the description fully compensates by documenting every parameter: image with example, command with allowed types (string or list), and extra_kwargs with common keys, value formats, and examples (name, environment, ports, volumes, labels). It even warns against guessing unknown keys and points to docs_lookup, which is excellent semantical guidance.

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's function: 'Create a container from an image without starting it.' It explicitly distinguishes this from container_run (create-and-start immediately) and container_start (starting an existing container), making the resource and scope unambiguous.

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 provides explicit when-to-use guidance: configure a container with extra_kwargs before first start, or want separate observable steps. It names alternatives directly: use container_run for create-then-start-immediately and container_start to start the created container. It also tells users to call docs_lookup for unsupported extra_kwargs, preventing guesswork.

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