Thermal Load Estimator for data centres / critical facilities. Returns total heat load, cooling capacity, units, airflow, chiller size, PUE estimate, power density, and a CRAC/CRAH/DLC/immersion comparison.
| Name | Required | Description | Default |
|---|---|---|---|
| it_load_kw | Yes | ||
| redundancy | Yes | ||
| lighting_kw | No | default 5 | |
| cooling_type | Yes | ||
| room_area_m2 | Yes | ||
| ambient_temp_c | Yes | ||
| ups_efficiency_pct | Yes | 1-99.9 |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of disclosing behavioral traits. It only says the tool 'Returns' values, which implies a read-only calculation, but it does not state whether there are side effects, assumptions, accuracy limitations, or any state changes. For an unannotated tool, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, dense sentence that front-loads the tool's purpose and lists its outputs without wasted words. It is efficient and immediately scannable by an agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 7-parameter calculator with no output schema, the description is not complete enough. It lists return values but omits the meaning of most inputs, does not explain assumptions or units beyond a vague 'units', and leaves the agent to infer critical details from the schema alone, which is also sparse.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is low at 29%, and the description does not explain the required parameters such as it_load_kw, redundancy, ambient_temp_c, or ups_efficiency_pct. The only partial addition is the CRAC/CRAH/DLC/immersion comparison, which weakly maps to the cooling_type enum, but this does not compensate for the overall lack of parameter guidance.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as a thermal load estimator for data centres / critical facilities and enumerates the specific outputs it returns (heat load, cooling capacity, PUE, etc.). However, it does not explicitly differentiate itself from the sibling tool estimate_cooling_load, so the distinction is left mostly to the output list.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'for data centres / critical facilities' provides clear context on when the tool applies. It does not explicitly mention when not to use it or name alternatives such as calculate_pue or estimate_cooling_load, but the domain scoping is unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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 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.
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.
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.
