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111-test-111

Math MCP Server

by 111-test-111

geometry_calculator

Calculate geometric properties like area, volume, and perimeter for shapes including circles, triangles, spheres, and cubes using dimensions or coordinate points.

Instructions

Brief description: Powerful geometry calculation tool, supporting plane geometry, solid geometry, and analytical geometry calculations.
Examples:
    geometry_calculator(shape_type='circle', operation='properties', dimensions={'radius': 5})
    geometry_calculator(shape_type='triangle', operation='area', points=[[0,0], [3,0], [0,4]])

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
shape_typeYesType of geometric shape. Supports: 'circle', 'triangle', 'rectangle', 'polygon', 'ellipse', 'sphere', 'cube', 'cylinder', 'cone', 'pyramid'
operationYesGeometric operation. Supports: 'area', 'volume', 'surface_area', 'circumference', 'perimeter', 'properties', 'distance', 'angle'
dimensionsNoDictionary of dimension parameters, e.g. {'radius': 5}, {'length': 10, 'width': 5}
pointsNoList of coordinate points for coordinate-based calculations
precisionNoNumber of decimal places for results
unitNoMeasurement unit identifierdefault
Behavior2/5

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

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It does not mention whether calculations are exact or approximate, error handling, or result format beyond examples.

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?

Description is short with clear examples. The first sentence provides purpose, and examples are illustrative. Minor oddity: 'Brief description:' label, but overall efficient.

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; description does not explain return values, error handling, or edge cases. For a tool with many shapes and operations, more completeness would help, but schema covers inputs well.

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 description coverage is 100%, so baseline is 3. The description adds value with examples showing real parameter combinations (e.g., radius for circle, points for triangle), enhancing understanding beyond the 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 explicitly states it handles plane, solid, and analytical geometry calculations, supported by clear examples. It distinguishes from sibling math tools like basic_arithmetic and calculus_engine.

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 use for geometry problems but does not explicitly guide when to use this tool vs alternatives among sibling math tools. No exclusions or alternative tool mentions.

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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