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image_list

Read-only

List Docker images stored locally on the daemon, with optional filters for repository, intermediate layers, or attributes like dangling. Get a quick summary of each image's ID, tags, size, and labels.

Instructions

List images in the daemon's local store.

Local only - for a registry's contents use registry_tags / hub_tags, and image_search to find images on Docker Hub. Dangling (untagged) build leftovers show with filters={"dangling": True}.

Args: repository: Only show images of this repository all: Show intermediate image layers filters: Filter by attributes (label, dangling, before, since, etc.)

Returns: list: One summary dict per image ({"Id", "RepoTags", "RepoDigests", "Created", "Size", "Labels", ...}); use image_inspect for a full inspect payload

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
allNo
filtersNo
repositoryNo

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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context beyond annotations: the local-only scope, dangling image visibility with filters, and the summarized return shape. It also points to image_inspect for richer data, which helps set expectations about the output.

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 and front-loaded with the core purpose, then alternatives, parameter details, and return format. Every sentence earns its place, and formatting with Args/Returns sections makes it easy for an agent to parse.

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 read-only list tool, the description covers purpose, scope, alternatives, parameter semantics, and return format, plus a pointer to a deeper inspection tool. With readOnlyHint/destructiveHint annotations already covering side effects, nothing essential is missing.

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 carries the full burden of explaining parameters. It provides meaningful explanations for all three parameters: repository limits the repository, all shows intermediate layers, and filters supports attributes like label, dangling, before, and since. This goes well beyond the bare schema types.

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: 'List images in the daemon's local store.' It clearly distinguishes this tool from registry-focused siblings by explicitly noting 'Local only' and naming registry_tags, hub_tags, and image_search as alternatives. This makes the tool's 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 gives explicit when-to-use guidance by contrasting local images with registry contents and Docker Hub searches. It also recommends image_inspect for full payloads, providing a clear routing decision to a sibling tool. This is strong, actionable usage 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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