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abellmugano

quantity-engine

by abellmugano
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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quantity_execute

Execute dimensional calculations and unit conversions with audit evidence, returning results, precision metadata, and reproducibility hashes to prevent silent unit errors in agent chains.

Instructions

Execute dimensional calculation and unit conversion with audit evidence. Input: sequence of typed operations (mul, add, sub, div, convert). Output: result value with unit, precision metadata, and reproducibility hash, or a structured failure.

Input Schema

NameRequiredDescriptionDefault
precisionNo
operationsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.1.1
    • changedInput schema / properties / operations / items / properties / b / description
      Previous value: -"Required for mul"New value: +"Required for mul, add, sub, div"
    • changedInput schema / properties / operations / items / properties / op / enum
      Previous value: -[
-  "mul",
-  "convert"
-]New value: +[
+  "mul",
+  "add",
+  "sub",
+  "div",
+  "convert"
+]
  2. First observedv0.1.0

TDQS

B3.2/5.0
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 it does disclose the return shape (value with unit, precision metadata, reproducibility hash, or structured failure) and implies deterministic behavior via the hash. It omits whether the operation is pure/read-only, how failures are triggered, and any validation rules, leaving notable behavioral gaps.

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

Conciseness4/5

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

Two sentences, front-loaded with the purpose followed by a compact input/output summary; no filler. Slightly terse given the nested schema, but structurally clean.

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

Completeness3/5

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

No output schema exists, and the description does cover return values, which is a plus. However, for a tool with a nested operations array and an undocumented operand $ref, the description leaves the caller without enough detail on how to assemble operations correctly.

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

Parameters3/5

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

Schema description coverage is 0% at the top level and the tool has a nested operations array plus a precision object, so the description should compensate more. It names the operation types (mul/add/sub/div/convert) and references precision metadata, but does not explain operand structure, the precision.mode/scale fields, or the 'to'/'at' semantics.

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

Purpose4/5

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

States a specific verb (Execute) and resource (dimensional calculation and unit conversion) with the audit-evidence angle, and enumerates the operation types. No siblings exist to differentiate, but the purpose itself is unambiguous.

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 guidance on when to choose this tool, prerequisites, or exclusions; the 'Input/Output' phrasing describes the contract rather than usage conditions. An agent learns what to send, not when this is the right call.

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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