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Glama

engineering

contractor_vetting

NIS2 / EPBD contractor vetting score from a 12-question checklist across financial, technical, cyber (NIS2) and energy (EPBD) categories. Flags blocking (critical) failures and returns recommended additional checks. Not a formal due-diligence report.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
answersYesMap of question_id -> yes|no|unknown. IDs: registered, insurance, no_prosecutions, certifications, references, cvs, cyber_policy, incident_reporting, access_control, assessor_cert, energy_qual, reno_experience
purposeNoVetting purpose

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A3.6/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Because no annotations are present, the description carries the full burden and does disclose that the tool computes a score, flags critical failures, and returns recommended checks. It does not explain how 'unknown' answers are handled, how the score is calculated, or whether the operation persists any data.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with no filler: the main deliverable comes first, the checklist scope second, and the key caveat last. Every sentence contributes meaningful information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description gives a useful high-level view of what the tool returns, which matters because there is no output schema. It does not specify the score scale, how blocking failures are determined, or how the purpose parameter changes behavior, leaving some uncertainty for a 12-question scoring tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already documents all parameters and answer IDs, but the description adds useful meaning by explaining that the checklist spans financial, technical, cyber (NIS2), and energy (EPBD) categories. It also hints that purpose selects the NIS2 vs EPBD lens, which adds context beyond the schema's bare 'Vetting purpose' text.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific deliverable—a NIS2/EPBD contractor vetting score—and its source (12-question checklist) and primary outputs (blocking failure flags and recommended checks). It does not explicitly distinguish itself from sibling tools like assess_epbd_score or check_nis2_readiness, so it stops short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'contractor vetting' plus the NIS2/EPBD scope clearly implies when this tool is relevant, and the caveat 'Not a formal due-diligence report' sets a boundary. However, there is no explicit when-not-to-use guidance or comparison against alternative sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

B3.1/5.0
Disambiguation3/5

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 Consistency2/5

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.

Tool Count2/5

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.

Completeness3/5

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.

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