PricingLab

describe_model

Inputs (name, unit, default, range, demo-server cap), outputs and extra functions of one model.

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": {
      "model": "<model>"
    },
    "name": "describe_model"
  }
}

Input Schema

Name	Required	Description	Default
`model`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 10, 2026

TDQS

C2.4/5.0

Behavior2/5

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

No annotations are provided, so the description must carry the full behavioral burden. It lists the kinds of information returned, but does not state that this is a read-only operation, what happens for an unknown model, or any permission/rate-limit behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very short and front-loads the content categories, but it is a sentence fragment with a parenthetical list. Terms such as 'demo-server cap' are not explained, so the structure is efficient but not fully clear.

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?

The tool is simple (one required parameter) and has an output schema, so return-value detail is not required in the description. However, it omits the model identifier format and any usage context, leaving key gaps for an agent trying to call it correctly.

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

Parameters2/5

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

The only parameter is 'model', and schema description coverage is 0%. The description says 'one model' but does not explain whether this is a model name, ID, slug, or example value, so it adds little meaning beyond the parameter name.

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

Purpose3/5

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

The description is a noun phrase rather than a clear verb+resource statement, though it implies the tool returns metadata about a single model: inputs, outputs, and extra functions. It gives enough content to infer the purpose, but lacks an explicit action such as 'returns' or 'describes', leaving the purpose only partially stated.

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?

There is no explicit when-to-use or when-not-to-use guidance. The phrase 'one model' hints that this is the single-model detail tool versus list_models, but the description does not name alternatives or state selection criteria.

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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