Skip to main content
Glama
crisjonblvx

enrichrapi-mcp

by crisjonblvx

convert_units

Convert numeric values between units of measurement for length, weight, temperature, area, and volume. Specify value and units to get the result.

Instructions

Convert a value between units of measurement.

Supports length, weight, temperature, area, and volume. Category is auto-detected from unit names.

Args: value: The numeric value to convert from_unit: Source unit (e.g. "km", "kg", "celsius", "gal", "ft2") to_unit: Target unit (e.g. "mi", "lb", "fahrenheit", "l", "m2") category: Optional: "length", "weight", "temperature", "area", "volume"

Returns: dict with keys: value, from_unit, to_unit, result, category, cost_usd

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
valueYes
to_unitYes
categoryNo
from_unitYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the full responsibility for behavior. It clearly states that category is auto-detected from unit names, explains that category is optional, and enumerates the return dictionary keys. This gives a solid account of what the tool does and what the caller can expect.

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 compact and well-structured: a one-sentence purpose, a one-line supported-category summary, an auto-detection note, then Args and Returns sections. Every sentence earns its place with no filler or redundant restatement of the tool name.

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 description provides enough to call the tool correctly: all parameters are explained and the return shape is listed despite no output schema. It does not list all supported unit strings or failure behavior for invalid/cross-category conversions, so it is not fully exhaustive, but it is complete for typical usage.

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 description coverage is 0%, but the description fully compensates by documenting every parameter with meaning and examples: value, from_unit ('km', 'kg', 'celsius'), to_unit ('mi', 'lb', 'fahrenheit'), and the optional category. It also clarifies the auto-detection behavior, adding value well 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 opens with a specific verb and resource: 'Convert a value between units of measurement.' It then enumerates the supported categories (length, weight, temperature, area, volume), which clearly distinguishes it from sibling tools like convert_currency and convert_timezone.

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 description implies when to use this tool by listing supported measurement categories, but it does not explicitly name alternative tools or state when not to use it. The sibling context includes convert_currency and convert_timezone, but the description never calls these out or provides exclusion criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.