Skip to main content
Glama

quantity-takeoff__calculate_takeoff

Calculate auditable construction material takeoffs from geometry or measurement payloads. Returns net, waste-adjusted, and purchase quantities for planning estimates.

Instructions

[quantity-takeoff — auditable construction material takeoffs (locked waste + purchase rounding)] Calculate an auditable material takeoff from explicit geometry or a supported SmartScale/ProtoGen measurement payload. Returns net, explicit waste-adjusted, and conservative purchase quantities. This is a planning estimate, not a guaranteed material order or professional certification.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemsYes
optionsNo
Behavior4/5

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

With no annotations, the description fully carries the burden. It discloses auditable nature, locked waste and purchase rounding, return of multiple quantity types, and the planning-estimate caveat. It does not explicitly mention idempotency or state modification, but the 'calculate' verb implies read-only.

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?

The description is concise with two sentences and a parenthetical note. It front-loads the tool's purpose and context, but structure could be improved by separating parameter guidance from output description.

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?

Given no annotations or output schema, the description should be more thorough. It covers input sources, output types, and a caveat, but lacks details on parameter formats, return structure, and potential side effects. For a complex tool handling geometry and payloads, this is insufficient for an agent to correctly invoke it.

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

Parameters2/5

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

Schema coverage is 0%, yet the description adds minimal parameter details. It mentions input types (geometry or measurement payload) but does not explain the structure of the 'items' array or 'options' object. The schema shows loose object types, and the description fails to specify required properties or formats.

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 the tool calculates an auditable material takeoff from explicit geometry or SmartScale/ProtoGen payloads. It specifies outputs (net, waste-adjusted, purchase quantities) and distinguishes from siblings like get_assembly and verify_result by focusing on calculation from raw inputs.

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 indicates when to use the tool (explicit geometry or SmartScale/ProtoGen payload) and clarifies it is a planning estimate, not a final order. However, it does not explicitly compare with sibling tools or state when not to use it.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/jdhart81/viridis-agent-fleet'

If you have feedback or need assistance with the MCP directory API, please join our Discord server