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Random Dog Image

dogceo.breeds.random_image
Read-onlyIdempotent

Get one or more random dog images, optionally filtered by breed and sub-breed (e.g. "hound" + "afghan"). Returns direct image URLs from the Dog CEO open-source image database. No auth required, MIT licensed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
breedNoFilter by dog breed (e.g. "hound", "labrador"). Omit for a random breed.
countNoNumber of random images to return (1-50, default 1)
sub_breedNoFilter by sub-breed within the given breed (e.g. "afghan" for breed "hound"). Requires breed to also be set.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoPresent only when the call failed. Includes error code, message, request_id, and any provider-specific extras.
resultNoTool response payload. Shape varies per tool — consult the tool description and inputSchema. May be an object, array, string, or number depending on the upstream provider response.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is fully covered. The description adds useful context: it returns direct image URLs, comes from the Dog CEO open-source image database, requires no auth, and is MIT licensed. This goes beyond the annotations without contradicting them.

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?

Two sentences with no filler. The main action and optional filtering are front-loaded, followed by return type, data source, and licensing/auth info. Every sentence earns its place.

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?

The tool is simple, has an output schema, and annotations cover safety. The description covers the return type (direct image URLs), the data source, and auth/licensing. It doesn't mention pagination or error cases, but for a simple random-image tool with an output schema, this is sufficient.

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%, so the schema already documents all three parameters (breed, count, sub_breed) with examples and constraints. The description adds the example pairing of 'hound' + 'afghan' and clarifies the sub-breed dependency, but the schema already covers the essential semantics. Baseline 3 is appropriate.

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 states a specific verb ('Get'), a clear resource ('random dog images'), and the optional filtering by breed and sub-breed with a concrete example. It distinguishes itself from sibling tools like dogceo.breeds.list and dogceo.breeds.sub_breeds by focusing on random image retrieval.

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 clearly explains the core use case and the optional breed/sub-breed filtering, and notes that omitting breed yields a random breed. It doesn't explicitly name sibling alternatives or state when not to use it, but the context is clear enough for an agent to select it appropriately.

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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