docker-hub-mcp

batch_image_stats

Retrieve pull counts, stars, and status for up to 100 Docker Hub images in parallel by passing a list of image names.

Instructions

Batch lookup metadata for up to 100 Docker Hub images in parallel. Pass a list of image names (e.g. ['nginx', 'python', 'grafana/grafana']). Returns a summary table with pulls, stars, and status for each.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "images": "<images>"
    },
    "name": "batch_image_stats"
  }
}

Input Schema

Name	Required	Description	Default
`images`	Yes	List of Docker image names (max 100).

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 3, 2026

TDQS

A4.4/5.0

Behavior4/5

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

With no annotations, the description carries the full burden. It discloses key behaviors: parallel execution, a maximum of 100 images, and the output format (a summary table with pulls, stars, and status). It does not mention auth or error handling, but for a metadata lookup these are not critical.

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 two sentences, front-loaded with the core purpose, then includes input format and output details. Every sentence earns its place, with no extraneous information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with one parameter and no output schema, the description covers operation, constraints, input format, and return summary. It omits error behavior, but that is not essential for a batch lookup. It is sufficiently complete given the tool's simplicity.

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

Parameters4/5

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

The schema covers the single parameter at 100%, so the baseline is 3. The description adds value with concrete examples (['nginx', 'python', 'grafana/grafana']) and clarifies the expected list format, which goes beyond the schema's basic 'List of Docker image names (max 100)'.

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 a specific action ('batch lookup metadata') on a specific resource (Docker Hub images), with a scope limit ('up to 100') and parallel execution. This distinguishes it from sibling tools like get_image_stats (single image) and search_images.

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 provides clear usage context: it explains to pass a list of image names and gives an example. It does not explicitly name alternatives or exclusion criteria, but the batch nature and sibling tool names imply when to use this tool. It lacks explicit 'when not to use' 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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