Calculate operational + embodied carbon and net-zero gap (CIBSE TM65 / RICS WLCA). Returns carbon intensity, benchmark, and prioritised reduction measures with payback.
| Name | Required | Description | Default |
|---|---|---|---|
| country | Yes | ISO-2: gb, de, fr, nl, es, pl, ro, it, ae, us | |
| building_type | Yes | office, residential, retail, industrial, hospital, warehouse, hotel | |
| floor_area_m2 | Yes | ||
| energy_kwh_year | No | Measured annual energy; 0 to use benchmark | |
| construction_year | No |
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 behavioral burden. It does disclose the outputs (carbon intensity, benchmark, prioritised reduction measures with payback), but it omits assumptions, default behavior for missing optional parameters, and any limitations or data requirements.
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?
A single sentence that front-loads the core purpose, then lists the return outputs without waste. The standard references add useful precision without bloat.
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?
The description states what the tool returns, which is useful given there is no output schema, but it omits required parameter semantics, default behaviors, and sibling differentiation. For a five-parameter calculator with no annotations, this is minimally adequate but leaves meaningful gaps.
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 60%, leaving floor_area_m2 and construction_year undocumented. The description does not compensate by explaining parameter meaning, units, or defaults such as using benchmark values when energy_kwh_year is absent. It adds little semantic value beyond the schema.
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 verb and resource: calculating operational and embodied carbon plus the net-zero gap, and cites CIBSE TM65 / RICS WLCA. This is clear and specific, though it does not explicitly differentiate itself from siblings like building_carbon_footprint or eu_cost_benchmarking.
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 use this tool over related carbon or benchmarking tools. It neither states typical use cases nor names alternatives, so an agent must infer selection context from the sibling list alone.
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.
