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tensorart-mcp

tensorart-mcp

by giacomodiguida
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

estimate_credits

Calculate how many credits a text-to-image job will cost before running it, based on prompt, dimensions, steps, sampler, model, and LoRA settings.

Instructions

Ask TAMS how many credits a text-to-image job would cost, without running it.

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": {
      "prompt": "<prompt>"
    },
    "name": "estimate_credits"
  }
}

Input Schema

NameRequiredDescriptionDefault
countNo
lorasNo
stepsNo
widthNo
heightNo
promptYes
samplerNoeuler
model_idNo
cfg_scaleNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the full burden and does disclose the key non-execution behavior. However, it omits auth requirements, whether the estimate is exact or approximate, rate limits, side effects, and return characteristics.

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 front-loaded sentence with no wasted words. It directly states the action, subject, and non-execution constraint.

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

Completeness2/5

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

Given 9 parameters, no annotations, no output schema, and 0% schema description coverage, a one-sentence description is insufficient. It communicates the high-level purpose and non-execution behavior but leaves parameter usage and operational details undocumented.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 9 parameters with 0% description coverage, so the description must compensate. It never explains prompt, count, loras, steps, width, height, sampler, model_id, or cfg_scale, nor how they affect the credit estimate.

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 uses a specific verb and resource ('estimate_credits') and states the exact scope: estimating credits for a text-to-image job. The phrase 'without running it' clearly distinguishes it from siblings like generate_image.

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 clear context for use ('without running it'), implying this should be used before generation to check cost against generate_image. It does not explicitly name alternatives or state when not to use it.

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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