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container_create

Creates a container from an image without starting it, allowing configuration via extra_kwargs for later manual startup, separating creation from execution.

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 full inspect payload (not yet running)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
imageYes
commandNo
extra_kwargsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv2.0.0

TDQS

A4.8/5.0
Behavior4/5

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

The description clearly states the container is created but not started, and that the return value is the full inspect payload 'not yet running'. It also directs agents to docs_lookup for unsupported extra_kwargs instead of guessing. With only minimal annotations, this carries the burden well, though it could mention side effects like image pulling or permissions.

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 long but well-structured and every clause earns its place. The first sentence delivers the core purpose; usage guidance comes next; parameter examples and fallback lookup instructions are structured clearly. Given the zero-coverage schema, this verbosity is justified.

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?

The description covers the required parameter, optional parameters, common extra_kwargs formats, expected return value, and points to docs_lookup for unknown options. Since there is no output schema, the return description is sufficient. This is complete enough for an agent to use the tool 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?

Schema description coverage is 0%, but the description fully compensates: image gets a concrete example, command gets type clarification, and extra_kwargs gets a detailed list of common keys with exact value shapes (e.g., ports, volumes, and environment formats). This gives agents enough detail to call the tool without guessing.

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 states a specific verb and resource ('Create a container from an image without starting it') and immediately distinguishes this tool from container_run and container_start. This makes the tool's identity clear even among a large sibling list.

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 explicitly says when to use container_create (configuring with extra_kwargs before first start, or wanting separate observable steps), when not to (common create-then-start should use container_run), and what to do next (use container_start). This is explicit, actionable guidance.

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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