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hub_rate_limit

Read-only

Check remaining Docker Hub pull rate limit budget before large pulls. Uses a non-metered HEAD request to report limits, with optional credentials for higher per-account limits.

Instructions

Report the caller's remaining Docker Hub pull-rate-limit budget.

Sends a HEAD to the ratelimitpreview/test manifest (a HEAD isn't metered as a pull, so the check costs no budget) and reads the RateLimit-Limit / RateLimit-Remaining headers. Call it before a large compose_pull / image_pull to avoid hitting the cap mid-deploy. Credentials raise the limit and switch metering from per-IP to per-account; falls back to DOCKER_MCP_SERVER_REGISTRY_USERNAME / DOCKER_MCP_SERVER_REGISTRY_PASSWORD, does NOT read ~/.docker/config.json. Plans with no limit return no headers — reported as "unlimited": true.

args: username - Optional Hub username (overrides DOCKER_MCP_SERVER_REGISTRY_USERNAME) password - Optional Hub password/token (overrides DOCKER_MCP_SERVER_REGISTRY_PASSWORD) returns: dict - {"authenticated", "limit", "remaining", "window_seconds", "unlimited"}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
passwordNo
usernameNo
Behavior5/5

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

Annotations only declare readOnlyHint=true and destructiveHint=false. The description goes far beyond this by explaining the HEAD request mechanism, that it costs no pull budget, how credentials affect metering, the environment variable fallback, and that unlimited plans return no headers (reported as 'unlimited': true). This is rich behavioral disclosure.

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 purpose and uses each sentence to convey essential behavioral details. The args and returns sections are clearly formatted and concise, with no filler or repetition.

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?

Even without an output schema, the description lists the exact return keys, explains edge cases (unlimited plans, missing headers), and covers authentication behavior. Given the tool's moderate complexity, this is complete enough for an agent to select and invoke it 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?

The input schema provides only parameter names and defaults, with zero descriptions. The description compensates fully by explaining that username/password are optional, override the corresponding environment variables, and that passwords/tokens are accepted. This adds critical semantic meaning.

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 a precise statement: 'Report the caller's remaining Docker Hub pull-rate-limit budget.' This names a specific verb ('report'), a specific resource (pull-rate-limit budget), and immediately distinguishes this tool from sibling hub tools like hub_tags and hub_repo_info.

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 says 'Call it before a large compose_pull / image_pull to avoid hitting the cap mid-deploy,' giving a clear when-to-use scenario. It does not provide explicit when-not-to-use guidance or name alternatives, but the context is unmistakable.

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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