Skip to main content
Glama

image_load

Load a Docker image from a tarball produced by image_save, using in-band bytes or a server file path.

Instructions

Load an image from a tarball produced by image_save, from in-band bytes or a file on the server host.

Counterpart of image_save; when the image lives in a registry, image_pull is the normal route. Pass exactly one of data (tarball bytes in band) or from_file (a path on the server host, streamed straight to the daemon — preferred for anything but small images, since in-band bytes are base64-encoded by MCP). from_file is read by the server's user; ~ is expanded.

args: data - Tarball contents; exactly one of data/from_file from_file - Path to a tarball produced by docker save / image_save; exactly one of data/from_file returns: list - One full inspect payload per loaded image

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNo
from_fileNo
Behavior4/5

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

The description adds behavioral context beyond annotations: it explains that from_file is read by the server's user and that ~ is expanded, and that data is base64-encoded. Annotations only indicate non-readonly and non-destructive, so the description fills in important details about how the tool operates.

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 with a clear purpose statement, then detailed guidance on parameters, including rationale for preference. Every sentence is informative and adds value, with no redundancy.

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?

Given the tool has only 2 parameters, no required fields, and no output schema, the description covers all necessary information: parameter descriptions, usage rules, return type, and file path details. It is complete for an agent to select and invoke 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 coverage is 0%, but the description fully explains both parameters: data is tarball contents (in-band bytes), from_file is a path to a tarball produced by docker save/image_save. It also clarifies that exactly one must be passed, which is critical for correct usage.

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 the tool loads an image from a tarball produced by image_save, using either in-band bytes or a file on the server host. It distinguishes itself by mentioning image_save as counterpart and image_pull as alternative for registry images.

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 'Pass exactly one of data or from_file' and explains when to prefer from_file ('streamed straight to the daemon — preferred for anything but small images') and that in-band bytes are base64-encoded. It also notes that image_pull is the normal route for registry images, providing clear guidance on tool selection.

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/GavinLucas/docker-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server