Skip to main content
Glama

buildx_inspect

Read-only

Inspect a Docker builder instance to retrieve driver, status, and supported platforms. Optionally boot the builder if it isn't running.

Instructions

Inspect a builder instance.

Human-readable detail (driver, status, supported platforms) for one builder; buildx_list returns machine-parsed JSON for all builders. Does not raise on a non-zero CLI exit — inspect returncode/stderr in the result.

args: name - Builder name (defaults to the active builder) bootstrap - Boot the builder if it isn't already running returns: dict - {"returncode": int, "stdout": str, "stderr": str, "truncated": bool}. stdout is human-readable; parse with the agent or call buildx_list for JSON.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNo
bootstrapNo
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable behavioral context: 'Does not raise on a non-zero CLI exit — inspect returncode/stderr in the result' and clarifies that stdout is human-readable. This goes beyond basic safety hints, though it could further detail error scenarios for missing builders.

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 compact and well-structured, with clear sections for args and returns. Each sentence adds value: purpose, differentiation, exit code behavior, parameter explanations, and return format. No redundant content is present.

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 2-parameter tool with no output schema, the description is self-sufficient. It specifies the return dict structure, parameter defaults, and non-zero exit behavior, and even points to an alternative for JSON output. This covers all essential context an agent needs.

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?

The input schema has 0% description coverage, so the description carries the full burden. It documents both parameters: 'name' (defaults to the active builder) and 'bootstrap' (boot if not running), adding semantics that the schema omits. This fully compensates for the schema's lack of descriptions.

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 opens with 'Inspect a builder instance,' a specific verb+resource statement. It also differentiates from sibling buildx_list by noting it provides human-readable detail for one builder while buildx_list returns machine-parsed JSON for all builders. This leaves no ambiguity about the tool's purpose.

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 contrasts with buildx_list for JSON output, effectively guiding the agent on when to use which tool. It also explains the bootstrap parameter's role ('Boot the builder if it isn't already running'). This provides clear context for selecting the tool and using its options.

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