Engineering ROI calculator. Returns current manual cost, annual saving, net benefit, ROI %, payback months, 3-year NPV and a verdict.
| Name | Required | Description | Default |
|---|---|---|---|
| team_size | Yes | ||
| hourly_rate | Yes | ||
| manual_hours | Yes | manual hours per week | |
| software_cost | No | annual GBP (default 0) | |
| efficiency_gain_pct | Yes | <=90 | |
| implementation_time_months | 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?
There are no annotations, so the description carries the burden of behavioral disclosure. It does disclose that the tool computes and returns several financial metrics, which implies a non-mutating calculator. However, it does not mention assumptions, interpretation of the verdict, whether inputs are persisted, or any limitations such as the efficiency gain cap.
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 concise sentence that front-loads the tool's purpose and then lists the full set of returned values. There is no filler, repetition, or unnecessary detail; every part of the 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 solid list of outputs but is incomplete for an unannotated tool with no output schema. It does not explain the verdict, the meaning of 'current manual cost', or default assumptions. Core callability is present, but an agent would still have to infer several operational details.
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 50%, with team_size, hourly_rate, and implementation_time_months lacking descriptions. The tool's description focuses entirely on outputs and adds no explanation of parameters, their units, or their significance. It does not compensate for schema gaps or clarify how the inputs relate to the returned metrics.
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 an Engineering ROI calculator and enumerates the exact outputs it returns: manual cost, annual saving, net benefit, ROI %, payback months, 3-year NPV, and a verdict. This is a specific verb-plus-resource description that is easily distinguished from all sibling tools, none of which provide ROI calculations.
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 versus other tools. It does not state the intended scenario, prerequisites, or exclusions. While the name and sibling list imply it is the only ROI option, no explicit usage context or alternative selection guidance is provided.
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.
