Parametric construction cost benchmark across UK/EU/GCC by country, project type, specification, procurement, programme and BIM. Returns per-m2 and total cost ranges plus a cost breakdown. Indicative parametric estimate, validate against current cost indices.
| Name | Required | Description | Default |
|---|---|---|---|
| bim | No | ||
| country | Yes | Country | |
| programme | No | ||
| procurement | No | ||
| project_type | Yes | Project type | |
| floor_area_m2 | Yes | Gross floor area (m2), min 100 | |
| specification | 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 disclosure burden. It discloses that the tool is indicative, that it returns per-m2 and total cost ranges plus a breakdown, and that results should be validated externally. This is strong transparency for an estimation tool, though it could note assumptions or data currency.
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 tightly written sentences with no filler. The main purpose is front-loaded, followed by output details and a caveat. Every sentence contributes useful information.
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 provides a strong overview of purpose, inputs, outputs, and limitations despite having no output schema or annotations. The main gaps are the lack of an explicit mention of the required floor_area_m2 parameter and the absence of currency specification, which is notable for an international cost tool.
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 43%, and the description names many parameters (country, project type, specification, procurement, programme, BIM) but adds limited semantic depth beyond their names. It does clarify that floor area relates to both per-m2 and total cost outputs. This partially compensates for the sparse schema descriptions but not fully.
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 uses a specific, informative verb phrase ('Parametric construction cost benchmark') and names the geographic scope and the main dimensions it covers. It clearly distinguishes this tool from the energy, carbon, and regulatory siblings by focusing on construction cost outputs.
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 clear context for when to use the tool: it is an indicative parametric estimate that should be validated against current cost indices. It does not explicitly name alternatives or exclusions, but the sibling tools are in different domains, so the usage context is clear enough.
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.
