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Building heat loss / gain and energy

wai_building_energy
Read-only

Real heat loss and heat gain calculation of a building on the climate of its place (NASA POWER): design heating and cooling loads (kW, W/m²), monthly energy, heating / cooling plant selection. Rectangular block: length_m × width_m × storeys. use: residential, office, retail, hotel, hospital, school, industrial. Detailed up to 1,000 m² gross floor area; larger buildings get the summary (full study in Wai / WaiMAP). Answer with the returned numbers, not an estimate made before the call.

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": {
      "lat": "<lat>",
      "lng": "<lng>",
      "length_m": "<length_m>",
      "width_m": "<width_m>",
      "storeys": "<storeys>"
    },
    "name": "wai_building_energy"
  }
}

Input Schema

NameRequiredDescriptionDefault
latYes
lngYes
useNo
storeysYes
width_mYes
length_mYes
window_ratioNo
storey_height_mNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=true, so safety is covered; the description adds real behavioral context beyond that: it depends on an external climate dataset (NASA POWER), it degrades to a summary output above 1,000 m², and it only supports a rectangular block footprint. The added guidance to answer with returned numbers rather than a guess is a meaningful behavioral instruction. It does not disclose latency, failure modes for unsupported locations, or default assumptions when optional parameters are omitted.

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 content is front-loaded with the outcome (heat loss/gain, load figures, monthly energy, plant selection) followed by geometry, use categories, and size limits. It is dense and list-like but every sentence carries information. A minor cost is the telegraphic phrasing ('use: residential, office, retail…') which reads as shorthand rather than prose.

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?

With no output schema, the description usefully enumerates what comes back — design loads in kW and W/m², monthly energy, and plant selection — plus the size threshold at which detail is truncated. For an 8-parameter computational tool with 0% schema documentation, the remaining gap is input semantics: two optional parameters and all units/defaults are left unexplained.

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% across 8 parameters, so the description must carry the load. It does explain the geometry contract (length_m × width_m × storeys) and enumerates the valid values of use, and lat/lng are implicitly framed as the building's location for climate lookup. But window_ratio and storey_height_m — both materially affecting an energy calculation — are never mentioned, and no units or defaults are given for any parameter.

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?

The description names a specific verb and resource: real heat loss and heat gain calculation for a building, driven by NASA POWER climate data for its location, returning design heating/cooling loads (kW, W/m²), monthly energy, and plant selection. It also states the geometric model assumed (rectangular block: length_m × width_m × storeys), which sharply bounds what the tool does. It never explicitly contrasts itself with siblings like wai_design_building or wai_datacenter_energy, so it stops short of full sibling differentiation.

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?

There is an implicit usage constraint — detailed results only up to 1,000 m² gross floor area, with larger buildings falling back to a summary — and an explicit instruction to report the returned numbers rather than a pre-call estimate. However, no guidance is given on when to prefer this tool over siblings such as wai_design_building or wai_datacenter_energy, and no preconditions (permissions, required climate coverage) are stated.

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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