Skip to main content
Glama

container_list

Read-only

List Docker containers with filters for status, labels, or creation time. Supports including stopped containers and limiting results.

Instructions

List containers.

args: all - Show all containers, including stopped ones since - Only show containers created after this id or name before - Only show containers created before this id or name limit - Maximum number of results filters - Filter by attributes (e.g. status, label) sparse - Skip inspect calls and return less detail ignore_removed - Ignore containers removed during listing managed_only - Only return containers created by this MCP server (filters on the docker-mcp-server.managed label); combines with any filters given returns: list - A list of container attrs dicts

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
allNo
limitNo
sinceNo
beforeNo
sparseNo
filtersNo
managed_onlyNo
ignore_removedNo
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. Beyond that, the description adds behavioral context by explaining parameters like 'all' for stopped containers, 'sparse' for less detail, and 'ignore_removed' for race conditions. It also notes the return type as a list of container attrs dicts.

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 header and bullet-pointed parameters. It is concise yet informative, though the 'returns' line could be integrated more succinctly. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

While the description thoroughly covers all 8 input parameters and explains their effects, it lacks an output schema or detailed return structure. The return type 'list of container attrs dicts' is vague, leaving the agent uncertain about the exact fields. Given the lack of output schema, the description does not fully compensate.

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?

With 0% schema description coverage, the description fully compensates by providing clear, concise explanations for all 8 parameters (e.g., 'all - Show all containers, including stopped ones'). This adds essential meaning beyond the bare schema types and defaults.

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 'List containers,' immediately conveying the tool's purpose. The parameter details further specify scope and filtering. The name distinguishes it from other container-related tools like container_inspect or container_logs.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It does not mention when to prefer container_list over container_inspect or other listing tools, and there are no exclusions or context hints.

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