gapup-mcp

building_enrich

Enrich a location with European building intelligence: roof surfaces (m²), parking areas, solar-obligation status under French loi APER and loi Climat-Résilience, existing solar installations. Covers 628,000 scanned roofs and 91,800 parkings across 6 EU countries (FR, DE, IT, ES, BE, NL). Deterministic database lookup — no LLM, no generation, sub-second.

Input Schema

TableJSON Schema

Name	Required	Description
`lat`	Yes	Latitude (WGS84)
`lng`	Yes	Longitude (WGS84)
`async`	No	If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
`radiusM`	No	Search radius in metres (default 150, max 500)
`minAreaM2`	No	Only return roofs/parkings at least this large, in m²

Output Schema

TableJSON Schema

Name	Required	Description	Default
`query`	No
`roofs`	No
`summary`	No
`parkings`	No
`solarInstalled`	No

TDQS

A4.1/5.0

Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly states 'Deterministic database lookup — no LLM, no generation, sub-second,' which conveys predictable, fast, non-generative behavior. It also provides coverage statistics, giving a sense of limitations. However, it does not describe behavior for unmatched locations or potential error modes, which would make it fully transparent.

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?

The description is compact and well-structured, with the main purpose stated in the first sentence, followed by coverage data and behavioral characteristics. Every sentence adds value: purpose, scope/coverage, and performance/reliability. No fluff or repetition.

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

Completeness5/5

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

Despite having 5 parameters and no annotations, the description is sufficiently complete for a database lookup tool. It covers purpose, geographic scope (6 EU countries), specific data categories, legal context, and performance characteristics. The existence of an output schema means return values need not be described in text. The only minor gap is explicit usage alternatives, but that is already covered under usage guidelines.

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

Parameters3/5

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

The input schema has 100% description coverage for all 5 parameters, including lat/lng, async, radiusM, and minAreaM2. The tool description adds context like 'roof surfaces (m²)' and 'parking areas' that aligns with minAreaM2, but it does not substantially exceed what the schema already documents. Baseline of 3 is appropriate.

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

Purpose5/5

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

The description begins with a specific action verb ('Enrich') and identifies a clear resource ('a location with European building intelligence'), then lists concrete data types (roof surfaces, parking areas, solar-obligation status, existing solar installations). This clearly distinguishes it from broader sibling tools like real_estate_intel or geo_logistics_intel by specifying the exact domain.

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 description implies usage context through mentions of French solar obligations and EU coverage, but does not explicitly state when to use this tool versus alternatives or when not to use it. There are no exclusions or alternative tool references, leaving usage guidance implicit rather than direct.

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

C2.5/5.0

Disambiguation2/5

With 271 tools, many have overlapping purposes (e.g., multiple competitor intel tools, multiple financial modelers, multiple ESG auditors). Detailed descriptions help slightly, but the sheer volume creates confusion. Agents would struggle to select the right tool among many similar options.

Naming Consistency1/5

Tool names are wildly inconsistent: mix of English and French, snake_case and short phrases, some very generic (process, run, execute equivalents). No discernible naming convention (e.g., abm_architect vs. boundary_control vs. bp_narratif). This makes it hard to predict tool names.

Tool Count1/5

271 tools is far beyond typical well-scoped servers (3-15). This indicates an unfocused, over-bloated tool surface. Even for a general business intelligence server, this number is excessive and violates the principle of each tool earning its place.

Completeness2/5

Despite the large count, coverage feels scattered. Some domains (e.g., content, competitive intel) have many tools, while others (e.g., supply chain, HR) have gaps. The set lacks a coherent scope; it seems like a dump of many separate tool collections rather than a complete, curated surface.

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