Dicebear

List Styles

list_styles

Read-onlyIdempotent

List all available avatar styles. Returns style names and descriptions—use with generate_avatar to pick a style.

Example tool call

This call uses example arguments supplied in the tool’s input schema.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_styles"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description	Default
`count`	Yes	The number of available avatar styles
`styles`	Yes	List of available avatar style names

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedMay 8, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "count": {
+      "description": "The number of available avatar styles",
+      "type": "integer"
+    },
+    "styles": {
+      "description": "List of available avatar style names",
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    }
+  },
+  "required": [
+    "count",
+    "styles"
+  ],
+  "type": "object"
+}

Changed1 schema field changedMay 6, 2026
- addedInput schema / examples
```
Added value: +[
+  {}
+]
```
First observedApr 14, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering the safety profile. The description adds the return value content ('style names and descriptions'), which goes beyond annotations and clarifies the output.

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 primary function, and includes a practical usage note. Every clause earns its place with no redundancy.

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?

For a zero-parameter list tool with rich annotations and an output schema, the description fully covers the tool's behavior, return content, and how it fits into the broader avatar generation workflow.

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?

There are zero parameters, so the baseline for parameter semantics is 4. The description doesn't need to explain parameters, and the schema confirms the tool takes no input.

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 the verb ('List'), the resource ('all available avatar styles'), and the scope. It also explicitly connects the tool to generate_avatar, distinguishing its purpose from sibling tools.

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 phrase 'use with generate_avatar to pick a style' provides explicit contextual guidance on when to use this tool. It does not list exclusions or alternative tools explicitly, but the context is clear enough for a simple list operation.

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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