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intelli-verse-x

Intelliverse Router MCP Server

Official

Get budget and credit balances

get_credits

Retrieve LLM daily budget and media credit balances for an app ID, covering image, video, voice, audio, audiobook, and 3D wallets.

Instructions

LLM daily budget for the API key plus per-App-ID media credit wallets (image, video, voice, audio, audiobook, 3D).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
app_idNo
Behavior3/5

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

The description discloses what data is returned (budget and credit wallets), but since no annotations are provided, it lacks information on side effects, rate limits, or permission requirements. It implies a read operation but does not state it explicitly.

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 a single, dense sentence with no wasted words. It front-loades the core purpose and includes key details (types of media credits).

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of output schema and annotations, the description explains the return content but does not specify the structure or field names. It is adequate for a simple tool but leaves gaps for an agent needing detailed behavior.

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?

With 0% schema description coverage, the description partially compensates by indicating that the optional app_id parameter filters results to per-App-ID media credits. However, it does not explain the default behavior when the parameter is omitted or how other properties are affected.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly indicates the tool returns LLM daily budget and per-App-ID media credit wallets, specifying the resources involved. However, it does not explicitly use a verb like 'get' or 'retrieve', and could be more explicit about the action.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus siblings like list_apps or list_api_keys. There is no mention of prerequisites, context, or alternatives, leaving an AI agent to infer usage.

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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