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Glama

dogceo

by io.github.pipeworx-io

Random Breed Image

random_breed_image
Read-onlyIdempotent

Get one dog photo for a specific breed (e.g., 'golden_retriever', 'bulldog'). Returns image URL and breed name. Use when you need exactly one photo of a particular breed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
breedYesThe breed name (e.g. "hound", "labrador"). Use list_breeds to see valid values.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
hintNoHint message if breed not found
breedYesThe requested breed name
foundYesWhether the breed was found
image_urlYesURL of the breed image or null if not found

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "breed": {
+      "description": "The requested breed name",
+      "type": "string"
+    },
+    "found": {
+      "description": "Whether the breed was found",
+      "type": "boolean"
+    },
+    "hint": {
+      "description": "Hint message if breed not found",
+      "type": "string"
+    },
+    "image_url": {
+      "description": "URL of the breed image or null if not found",
+      "type": [
+        "string",
+        "null"
+      ]
+    }
+  },
+  "required": [
+    "breed",
+    "found",
+    "image_url"
+  ],
+  "type": "object"
+}
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "breed": "bulldog"
+  }
+]
  3. First observed

TDQS

A4/5.0
Behavior3/5

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. The description adds useful context about return values ('image URL and breed name') but does not disclose edge cases or non-obvious behavior. Given the annotation coverage, a score of 3 is appropriate.

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 long, front-loaded with the core purpose, and every sentence earns its place. No wasted words or irrelevant detail.

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 one-parameter, read-only tool with rich annotations and an output schema, the description covers purpose, return value, and usage context. A minor gap is lack of explicit behavior for invalid breed names, but the schema's reference to list_breeds mitigates this.

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?

The only parameter 'breed' is fully documented in the schema with examples and a pointer to list_breeds. Schema description coverage is 100%, so the description does not need to compensate; it only adds redundant examples to the schema.

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 uses a specific verb and resource: 'Get one dog photo for a specific breed' with concrete examples. It clearly distinguishes itself from generic random_image and plural breed_images by emphasizing 'one photo' of a 'specific breed'.

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 states when to use the tool: 'Use when you need exactly one photo of a particular breed.' It does not explicitly name alternatives, but the context is clear and the sibling list includes likely alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All dogceo toolsInspect
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TDQS

A3.7/5.0
Disambiguation2/5

Several tools deliberately overlap: ask_pipeworx and ask_pipeworx_beta are currently identical, and the dog-photo trio plus a cluster of six prediction-market tools creates real selection ambiguity. Although descriptions are detailed, an agent could easily call the wrong variant.

Naming Consistency3/5

All names are consistently snake_case and readable, with useful domain prefixes like polymarket_ and ask_pipeworx_. However, conventions are mixed: compare_entities is verb-first, entity_profile is noun-first, bet_research is object-verb, and random_image is adjective-noun.

Tool Count2/5

35 tools is too many for a server whose name suggests a simple dog-photo service, and most tools are unrelated to that identity. The scatter across dog images, deep data research, prediction markets, npm scanning, memory, and llms.txt generation makes the set feel bloated rather than comprehensive.

Completeness3/5

The dog-image functionality is complete, and the subscription and memory lifecycles have paired operations. However, the server's true domain is incoherent, so completeness is difficult to assess; there are no major dead-ends within each cluster, but the unrelated utility tools create large topical gaps relative to the apparent dogceo identity.

Resources