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Anam MCP Server

Anam MCP Server

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by anam-org
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Python
Remote

search_avatars

Find avatars by name with optional filter for stock-only avatars.

Instructions

Search avatars by name.

Fetches all avatars and filters by name match. Use this to find avatars like "Cara", "Mia", "Gabriel", etc.

Args: query: Name to search for (case-insensitive, partial match) stock_only: If True, only return stock avatars (not custom)

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "search_avatars"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYes
stock_onlyNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.13

TDQS

A4.6/5.0
Behavior4/5

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

With no annotations, the description carries full burden. It discloses the read behavior (fetch all then filter), case-insensitive partial matching, and the effect of stock_only. It does not detail pagination or performance implications, but the core behavior is well explained.

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 concise with a clear purpose statement, a brief behavior explanation, and a structured Args section. Every sentence adds value without 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?

Given the tool's simplicity (2 parameters, output schema present), the description covers the purpose, parameters with semantics, and the core filtering behavior. It is complete for an AI agent to understand and invoke 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?

Schema description coverage is 0%, but the description provides detailed explanations for both parameters: query as case-insensitive partial match, stock_only as filtering custom avatars. This adds significant value beyond the schema's type-only information.

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 'Search avatars by name' and explains it fetches and filters avatars. It distinguishes from siblings like list_avatars (which likely lists all without filtering) and search_voices (different resource).

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 gives examples of when to use ('find avatars like Cara, Mia, Gabriel') but does not explicitly mention when not to use or alternatives like list_avatars. The guidance is clear but not exhaustive.

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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