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Glama

Dockerhub

by io.github.pipeworx-io

Get Image

get_image
Read-onlyIdempotent

Get detailed metadata for a Docker Hub repository: description, pull count, stars, last updated date, and official status. Use before pulling an image to verify quality and currency.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesRepository name (e.g., "nginx", "redis")
namespaceYesRepository namespace — use "library" for official images (e.g., "library", "bitnami")

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
pullsYesTotal number of pulls
starsYesTotal number of stars
hub_userYesHub user or organization that owns the repository
full_nameYesFull repository name (namespace/name)
is_privateYesWhether the repository is private
descriptionYesShort description of the image
last_updatedYesISO timestamp of last update
full_descriptionYesFull detailed description of the image

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the description's role is lighter. It adds value by enumerating the metadata fields returned (description, pull count, stars, etc.), which aids the agent in understanding the tool's 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 consists of two concise sentences with no redundant information. It front-loads the purpose and follows with a usage guideline, making it efficient and scannable.

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 rich annotations (readOnlyHint, openWorldHint, etc.) and the presence of an output schema, the description covers the necessary context: what the tool does, what data it returns, and when to use it. No additional information is needed for an agent to invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with clear descriptions for both parameters (name and namespace). The description does not add additional semantic meaning beyond the schema, so baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves detailed metadata for a Docker Hub repository, listing specific fields. It implicitly distinguishes from search_images by focusing on a single known repository, but does not explicitly differentiate from sibling tools like get_tags or get_tag_details.

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

Usage Guidelines4/5

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

The description explicitly advises using the tool before pulling an image to verify quality and currency. It does not mention when not to use it or explicitly compare to alternatives, but the usage context is clear.

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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TDQS

A3.6/5.0
Disambiguation2/5

Many tools overlap in purpose, e.g., ask_pipeworx, ask_pipeworx_grounded, deep_research, entity_profile, and validate_claim all perform data lookups with subtle differences. Polymarket tools also have overlapping scopes. The large number of tools with similar functions creates confusion.

Naming Consistency3/5

Names are inconsistent: some use verb_noun (get_image, list_subscriptions), others are descriptive phrases (ai_visibility_check, bet_research), and some are single words (forget, recall). No clear pattern.

Tool Count2/5

33 tools is high, and many are unrelated to Dockerhub. The server name suggests a focused Docker toolset, but the bulk of tools are for Pipeworx/Polymarket/data lookups, making the count excessive for the advertised domain.

Completeness2/5

As a Dockerhub server, it lacks basic Docker operations like push, delete, or manage repositories. As a general data toolset, it covers many domains but still misses some core operations (e.g., no tool for searching inside images).

Resources