UAE Scope 1 + Scope 2 GHG estimate from electricity and gas use, with emirate-specific grid emission factors (Dubai/Abu Dhabi/national) and the Federal Decree-Law No. 11 of 2024 mandatory-reporting context. Grid factors carry base-year/confidence caveats.
| Name | Required | Description | Default |
|---|---|---|---|
| gas_m3 | No | Annual natural gas (m3), alternative to gas_kwh | |
| emirate | Yes | ||
| gas_kwh | No | Annual natural gas (kWh) | |
| electricity_kwh | Yes | Annual grid electricity (kWh) |
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, the description carries full responsibility for disclosing behavior. It does disclose a meaningful limitation ('Grid factors carry base-year/confidence caveats'), which adds transparency beyond the schema. However, it does not state what the tool returns (e.g., units, format, or whether a range is provided), leaving a notable gap for a calculation tool.
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 dense sentences with no filler. The primary function is stated first, followed by the legal context and a concise caveat. Every clause contributes unique information, and the description is appropriately sized for the tool's complexity.
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 covers the tool's purpose, key inputs, and a caveat, but with no output schema it omits the return format or units (e.g., kgCO2e vs tCO2e). The mutual exclusivity of gas_m3 and gas_kwh is handled by the schema, but an agent would still lack certainty about what the tool returns, which is important for correct invocation.
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 coverage is 75% and already documents three parameters, but the description adds crucial meaning for 'emirate' by naming Dubai/Abu Dhabi/national emission factors. It also maps 'electricity and gas use' to the relevant parameters. This goes beyond the raw schema, though gas_m3 vs gas_kwh semantics are already well covered by schema descriptions.
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 resource (UAE Scope 1 + Scope 2 GHG), a clear operation (estimate), and the inputs (electricity and gas use). It also includes geospatial and legal specificity (emirate factors, Federal Decree-Law) that distinguishes it from generic carbon tools like calculate_carbon or building_carbon_footprint.
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 implies the tool is for UAE mandatory-reporting contexts but gives no explicit when-to-use or when-not-to-use guidance, nor does it name alternatives among the many siblings. The legal context hints at usage, but an agent must infer when this tool is preferred over calculate_carbon or building_carbon_footprint.
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.
