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Mini Agent MCP

Mini Agent MCP

by Microbiosis
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
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unit_convert

Convert values between units in categories like length, weight, temperature, and data. Specify the value, source unit, and target unit to get the converted result.

Instructions

Convert between units of the same category. Categories: length (mm, cm, m, km, inch, in, ft, yard, yd, mile, mi), weight (mg, g, kg, ton, t, oz, lb, pound), temperature (C, F, K), data (bit, byte, KB, MB, GB, TB). Example: convert 100 from 'cm' to 'inch'.

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": {
      "value": "<value>",
      "from": "<from>",
      "to": "<to>"
    },
    "name": "unit_convert"
  }
}

Input Schema

NameRequiredDescriptionDefault
toYesTarget unit, e.g. 'inch', 'lb', 'F', 'MB'
fromYesSource unit, e.g. 'cm', 'kg', 'C', 'KB'
valueYesThe value to convert

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.1

TDQS

A4.2/5.0
Behavior3/5

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

No annotations are provided, so the description must fully disclose behavior. It lists supported units and categories but omits details like error handling for mismatched categories or invalid units, which are important for an AI agent to anticipate.

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 two sentences plus an example, making it very concise. Key information is front-loaded with the purpose immediately stated.

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

Completeness4/5

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

The tool is simple and the description covers categories and units adequately. No output schema exists, but the return format is implicitly clear for a conversion tool. Minor gap: no mention of precision or rounding.

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

Parameters4/5

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

Schema coverage is 100% with descriptions for each parameter. The description adds value by providing category groupings and a concrete example, enhancing understanding beyond the schema alone.

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 'Convert between units of the same category' and provides specific categories and examples. It distinguishes itself from sibling tools like calculator or random_gen by focusing on unit conversion.

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 explains the tool's purpose and gives an example, but it doesn't explicitly state when to use it over alternatives or provide any preconditions. However, the context is clear enough for most use cases.

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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