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RoofMeasure MCP

RoofMeasure MCP

Official
by thegimmieapp
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

measure_roof

Extract detailed roof measurements from satellite data: total area, facet count, pitch, and waste analysis in JSON format.

Instructions

Measure a roof from satellite data. Returns full JSON measurements: total area (sq ft + squares), facet count and detail, predominant pitch, areas per pitch, ridges/hips/valleys/rakes/eaves lengths (ft), waste table (squares with 0-32% waste), suggested waste factor, and steep-slope split.

Args: address: Full property address (street, city, state, zip).

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": {
      "address": "<address>"
    },
    "name": "measure_roof"
  }
}

Input Schema

NameRequiredDescriptionDefault
addressYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior2/5

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

No annotations provided, and the description only lists outputs and args. It does not disclose whether the operation is read-only, requires authentication, has rate limits, or what happens on invalid addresses.

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 front-loaded with the main action and then details output. It is appropriately sized, though the long list of output fields could be slightly condensed.

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 the complexity of roof measurement, the description covers return values well and has an output schema. However, it lacks behavioral context (idempotency, prerequisites, cost) and does not leverage annotations.

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 0%, but description fully explains the 'address' parameter: 'Full property address (street, city, state, zip).' This adds necessary meaning beyond the bare schema type.

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?

Description clearly states the tool measures a roof from satellite data and lists detailed output. It distinguishes from siblings (generate_roof_report, generate_xactimate_estimate) by focusing on raw measurements rather than reports or estimates.

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?

No explicit guidance on when to use this tool versus alternatives. The description does not mention when to prefer measure_roof over generate_roof_report or generate_xactimate_estimate.

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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