random_imageGet a random dog photo. Returns image URL and breed name. Use when you need any dog picture without a specific breed preference.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| image_url | Yes | URL of a random dog image |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {}
+]Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "image_url": {
+ "description": "URL of a random dog image",
+ "type": "string"
+ }
+ },
+ "required": [
+ "image_url"
+ ],
+ "type": "object"
+}Input schema / examplesRemoved value: -[
- {}
-]Output schema / (root)Previous value: -{
- "properties": {
- "image_url": {
- "description": "URL of a random dog image",
- "type": "string"
- }
- },
- "required": [
- "image_url"
- ],
- "type": "object"
-}New value: +nullOutput schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "image_url": {
+ "description": "URL of a random dog image",
+ "type": "string"
+ }
+ },
+ "required": [
+ "image_url"
+ ],
+ "type": "object"
+}Input schema / examplesAdded value: +[
+ {}
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds behavioral context by stating it returns an image URL and breed name, which goes beyond the annotations. Given the simplicity of the tool and the strong annotations, this is sufficient, though it could have mentioned the random selection behavior more explicitly.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that immediately states the action and result. Every word earns its place, with no unnecessary detail or filler. It is highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity, zero parameters, and rich annotations, the description is complete. It states the tool's purpose, usage context, and return value, which is all needed for an agent to select and invoke it correctly. The presence of an output schema further reduces the need to explain return details in the description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the schema provides complete coverage. According to the rubric, a baseline of 4 is appropriate for 0-parameter tools, and the description does not need to add parameter details. The description does not repeat parameter information, which would be redundant.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with a specific verb and resource ('Get a random dog photo') and clearly distinguishes itself from sibling tools like random_breed_image and breed_images by stating it returns any dog picture without breed preference. This fully clarifies its purpose and differentiates it from alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 any dog picture without a specific breed preference'), providing clear context. However, it does not explicitly mention when not to use it or name alternatives, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.