Estimate cooling load using CIBSE Guide B2 / ASHRAE methodology. Returns required capacity, installed capacity with redundancy, system recommendation, and annual energy.
| Name | Required | Description | Default |
|---|---|---|---|
| it_load_kw | Yes | Required for data_centre/server_room. Set 0 for others. | |
| redundancy | Yes | ||
| space_type | Yes | ||
| climate_zone | Yes | ||
| floor_area_m2 | Yes | ||
| occupancy_persons | Yes |
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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does disclose the output types (required capacity, installed capacity with redundancy, system recommendation, annual energy), but says nothing about assumptions, limitations, or how inputs affect behavior. An agent gets no warning about data-centre specifics, methodology sensitivity, or accuracy expectations.
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?
Two efficient sentences front-load the core purpose and then list outputs with no wasted words. It could be slightly better structured with usage guidance, but for what it includes, the prose is tight and scannable.
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 6-required-parameter tool with no output schema and no annotations, the description is too thin. It tells the agent what the tool returns but not how to populate the inputs correctly, when to choose it, or what methodological constraints apply. The high-level output list is useful but not sufficient.
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 only 17%, and the description does not compensate by explaining parameters like climate_zone, space_type, occupancy_persons, or floor_area_m2. It indirectly ties redundancy to 'installed capacity with redundancy' and implies the methodology, but most parameters remain semantically underdocumented.
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 names a specific action ('Estimate cooling load') with explicit methodology references (CIBSE Guide B2 / ASHRAE) and lists concrete outputs. It does not explicitly compare itself to sibling tools like thermal_load or heatpump_heatloss, but the methodology and output detail provide enough differentiation to be clear.
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 description gives no guidance on when to choose this tool over alternatives such as thermal_load or heatpump_heatloss. It implies the use case (cooling load estimation) but provides no exclusions, prerequisites, or decision rules.
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.
