Generate an indicative Technical Due Diligence (TDD) fee estimate. Returns preliminary and full DD fee ranges (GBP), turnaround days, and contact CTA.
| Name | Required | Description | Default |
|---|---|---|---|
| geography | Yes | ||
| disciplines | Yes | ||
| building_type | Yes | office, data_centre, industrial, retail, residential, mixed_use, hotel, warehouse, healthcare | |
| gross_area_m2 | Yes | ||
| timeline_weeks | Yes | ||
| transaction_type | 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, the description carries the full burden, and it does add value by disclosing that the tool returns indicative ranges and a contact CTA. However, it does not clarify whether calling the tool has side effects such as triggering contact, storing the request, or creating any commitment, leaving meaningful ambiguity.
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, front-loaded sentence that states the action and enumerates the key return values with no filler. It is appropriately sized for a straightforward calculator-style tool.
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 return envelope well given there is no output schema, and the required parameters are visible in the input schema. Still, with no annotations and low schema coverage, it leaves gaps around how inputs should be interpreted, what 'contact CTA' means behaviorally, and any limitations or exclusions for the estimate.
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 adds no parameter-level meaning beyond what the schema already names. Terms like gross_area_m2 and timeline_weeks are self-evident in isolation, but the description does not explain how disciplines, geography, or transaction_type affect the estimate, nor does it clarify units or constraints 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 uses a specific verb ('Generate an indicative') and a specific resource ('Technical Due Diligence (TDD) fee estimate'), and immediately names the main outputs: preliminary/full fee ranges, turnaround days, and contact CTA. It is clearly differentiated from the sibling calculators and checkers, none of which focus on TDD fee estimation.
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?
No guidance is provided on when to use this tool versus alternatives, nor on when not to use it. The description does not reference any sibling tool or state whether this is appropriate for early-stage budgeting versus a formal engagement.
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.
