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docker-mcp-server

docker-mcp-server

by L337-org
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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volume_list

Read-only

List Docker volumes with optional filters for dangling, name, or label; restrict to managed volumes when needed.

Instructions

List volumes.

Volumes are addressed by name only - feed a Name to volume_inspect for detail or volume_remove / volume_prune to clean up. filters={"dangling": True} finds volumes that no container references.

Args: filters: Filter by attributes (e.g. dangling, name, label) managed_only: Only return volumes created by this MCP server (filters on the docker-mcp-server.managed label); combines with any filters given

Returns: list: One volume document ({"Name", "Driver", "Mountpoint", ...}) per volume

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "volume_list"
  }
}

Input Schema

NameRequiredDescriptionDefault
filtersNo
managed_onlyNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv2.2.1
  2. Removedv2.1.4
  3. Addedv2.0.0

TDQS

A5/5.0
Behavior5/5

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

Annotations already establish readOnly=true and destructive=false, and the description adds meaningful behavior beyond that: return shape per volume, filter semantics with a dangling example, and the managed_only label behavior. No behavioral surprises are left hidden.

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 front-loaded with the core purpose, then gives routing guidance, parameter details, and return shape in compact labeled sections. Every sentence adds useful information with no filler.

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 simple list tool with two optional parameters and no output schema, the description covers purpose, usage, parameter semantics, and return format. An agent has enough to call it correctly and interpret results.

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 coverage is 0%, so the description carries the full burden for parameters. It documents filters with an example and attribute hints, and managed_only with precise label behavior and composition with filters. Both parameters are fully explained.

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?

Description begins with a specific verb and resource ('List volumes'), and clarifies its scope by pointing to sibling tools: volume_inspect for detail and volume_remove/volume_prune for cleanup. This distinguishes it clearly from nearby volume operations.

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?

It explicitly tells the agent that volumes are addressed by name, directs volume_inspect to detail and volume_remove/volume_prune to cleanup, and gives a concrete filters example for dangling volumes. This is actionable when-to-use guidance with alternatives.

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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