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zvex

zvex

by erzat1986
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

estimate_cost

Calculate credit cost for dubbing a video based on length and tier, and verify if your current balance covers it.

Instructions

Estimate the credit cost of dubbing a video and check the credit balance.

Pricing is a flat 10 credits per minute for every tier (a tier only changes which features are available, not the price).

Args: minutes: Video length in minutes (0 < minutes <= 600). tier: "fast", "standard" or "professional".

Returns: JSON with credits, current balance and whether the balance is enough.

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": {
      "minutes": "<minutes>"
    },
    "name": "estimate_cost"
  }
}

Input Schema

NameRequiredDescriptionDefault
tierNostandard
minutesYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

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 discloses its read-only nature (estimate, not actual dub), the pricing model, and the return contents (credits, balance, sufficiency). It could mention that no credits are consumed, but the term 'estimate' strongly implies that, making this sufficient for a safe read operation.

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 tightly structured: purpose first, then pricing rule, then parameter explanations, then return summary. Every sentence earns its place and there is no fluff. It is long enough to be informative but not bloated.

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 simple estimation tool with two parameters and a known output (already described), the description covers all necessary aspects: what it does, how pricing works, parameter constraints, and return behavior. The presence of an output schema reduces the need for further detail, but the description already goes beyond it.

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 coverage is 0%, but the description fully compensates: it explains minutes range (0-600) and valid tier values (fast/standard/professional), plus the flat pricing relationship that ties them together. This adds real meaning beyond the bare 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 clearly states the verb (estimate) and resource (credit cost of dubbing a video), plus a second purpose (checking credit balance). It is inherently distinct from siblings like submit_dubbing_job and get_job_status, so an agent can easily tell them apart.

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 purpose implies the use case: estimating cost and checking balance before submitting a job. While it doesn't explicitly name when not to use it or refer to alternatives, the context (sibling names) makes the intended usage clear. The flat pricing note adds useful whether-to-use information.

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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