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engineering

heatpump_heatloss

Domestic whole-dwelling fabric + ventilation heat-loss estimate for heat pump sizing (BS EN 12831 / MCS), from floor area, storeys, age band, location and glazing. Returns design heat loss (kW), recommended heat pump output, indicative SCOP and annual demand. Typical U-values/SCOP, not measured.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
storeysNo
age_bandYes
locationYes
flow_temp_cNoEmitter flow temperature
floor_area_m2Yes
air_change_rateNoACH, default 0.5
ceiling_height_mNoDefault 2.4
glazing_pct_of_floorNoDefault 25
external_design_temp_cNoOverride 99.6% external design temp
internal_design_temp_cNoDefault 21

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

No annotations are present, so the description carries the burden. It discloses that results are estimates based on typical U-values/SCOP rather than measured data, and it enumerates what the tool returns. This prevents over-trusting the outputs, though deeper assumptions behind age band and location are not detailed.

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

Conciseness5/5

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

Two dense sentences with no redundancy. The purpose, standard, key inputs, outputs, and caveat are all front-loaded and each clause contributes useful information.

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?

For a 10-parameter tool with no output schema, the description covers core return values, input categories, and the estimate caveat. It omits explicit mention of override parameters such as flow temperature and design temperatures, but those are schema-described and secondary to the core calculation.

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 60%, so the schema already documents several parameters. The description adds meaning by identifying the governing physical inputs: floor area, storeys, age band, location, and glazing. It also links them to the calculation purpose without merely restating parameter names.

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 states a specific verb and resource: 'Domestic whole-dwelling fabric + ventilation heat-loss estimate' for 'heat pump sizing', tied to BS EN 12831 / MCS. It also lists concrete outputs, making it clearly distinguishable from cooling-load or general thermal tools.

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 usage context is clear: domestic, whole-dwelling, heat pump sizing, using standard assumptions. It does not explicitly name sibling alternatives or say when not to use it, but the purpose and domain are specific enough to route an agent correctly.

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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TDQS

B3.1/5.0
Disambiguation3/5

Many tools have distinct domains (structural, carbon, compliance, heat pumps), but several overlap at a surface level: calculate_carbon, building_carbon_footprint, and uae_climate_ghg all deal with carbon; estimate_cooling_load and thermal_load both compute cooling loads; and multiple UK/UAE compliance checkers have similar 'readiness/checker/precheck' names. Descriptions help differentiate, but an agent could still select the wrong tool without careful reading.

Naming Consistency2/5

Naming is a mix of verb-led patterns (assess_epbd_score, calculate_carbon, check_uae_bim_compliance, estimate_cooling_load, get_technical_dd_quote) and noun-led phrases (building_carbon_footprint, building_readiness, digital_renovation_passport, roi_calculator, thermal_load). Sub-groups like check_* and eurocode_* are consistent internally, but the overall set has no unifying convention, which adds cognitive load.

Tool Count2/5

With 27 tools, the server exceeds the 'heavy' range, even though the engineering domain is broad. Many tools are highly specialized (e.g., part_s_ev, mees_checker, dgnb_bim_readiness), and the large count risks overwhelming an agent trying to pick the right one. The scope may justify the number, but it edges into too-many territory.

Completeness3/5

The server covers a wide range of building and sustainability assessments: carbon, energy, compliance (EU/UK/UAE), structural design, cost benchmarking, and data centres. However, there are gaps in adjacent areas common to building engineering—such as acoustic design, water/sanitation, electrical systems, or thermal bridging—which would be expected from a general 'engineering' server. It is reasonably complete for its apparent sustainability/regulatory focus, but not universally.

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