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tokencast

tokencast

by krulewis
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

get_calibration_status

Check calibration health for pre-execution cost estimates to ensure accuracy metrics remain valid.

Instructions

Get calibration health and accuracy metrics for cost estimates

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_calibration_status"
  }
}

Input Schema

NameRequiredDescriptionDefault
windowNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.4

TDQS

B3.3/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden of behavioral disclosure. As a 'get' operation, it implies read-only behavior, and 'health and accuracy metrics' adds some context. But it does not mention data format, side effects, or prerequisites beyond the name.

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, focused sentence that front-loads the action and object. Every word contributes meaning, and there is no fluff or redundancy.

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?

With no output schema or annotations, the description leaves key details undefined: the meaning of the 'window' parameter, the exact nature of the returned metrics, and any behavioral caveats. While the tool is simple, the missing parameter documentation makes it incomplete.

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 schema has one parameter ('window') with 0% description coverage, and the description does not mention it at all. The agent has no way to know what 'window' accepts or means, making this a critical gap that the description fails to compensate for.

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 ('Get') and resource ('calibration health and accuracy metrics for cost estimates'), clearly distinguishing this from sibling tools like estimate_cost or get_cost_history. It immediately conveys the tool's purpose without ambiguity.

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

Usage Guidelines3/5

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

The usage is implied: one would call this to review calibration status. However, there is no explicit guidance on when to use it versus alternatives, nor any exclusion criteria. It lacks clear context about the intended scenario.

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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