gapup-mcp

real_estate_intel

Read-onlyIdempotent

Real estate intelligence aggregator with a best-in-class French dataset (DVF — Demandes de Valeurs Foncières — 100% of FR transactions since 2019, public, keyless) plus UK Land Registry Price Paid (all UK transactions 1995+). Four modes: (1) property — full transaction history for a specific address; (2) comparables — median/std price/m² within a radius (default 500m); (3) market — annual price series, YoY change, volume, trend by commune; (4) valuation — two-method estimate (comparables median + hedonic regression if n≥30) with confidence scoring (high/medium/low). All sources are free and require no API key. ICP: PropTech agents, REITs, fund managers, family offices, insurance. SLA: ≤25s p95 (sources fetched in parallel, 8s budget each). Cache: 24h TTL (DVF data is stable). Quality score: 30 pts DVF retrieved, 20 pts geocoding, 20 pts UK LR retrieved, 15 pts if comparables count ≥10, 15 pts if method quality achieved. Status: failed/<60/≥60 → failed/partial/final. No env vars required.

Input Schema

TableJSON Schema

Name	Required	Description
`mode`	Yes	property: transactions at an address \| comparables: sample around a point \| market: commune/neighbourhood market stats \| valuation: price estimate for a given surface
`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.
`date_to`	No	ISO date YYYY-MM-DD — latest transaction date
`location`	Yes	Location descriptor. One of: {address, city?, country?} \| {lat, lon, radius_m?} \| {insee_code} for FR communes.
`date_from`	No	ISO date YYYY-MM-DD — earliest transaction date
`max_results`	No	Maximum number of results to return (5–50, default 20)
`surface_max`	No	Maximum surface in m² (±20% tolerance applied for comparables)
`surface_min`	No	Minimum surface in m² (±20% tolerance applied for comparables)
`property_type`	No	Filter by property type (default: all)

Output Schema

TableJSON Schema

Name	Required	Description
`mode`	Yes
`market`	No	mode=market — commune-level market stats
`status`	Yes
`sources`	Yes
`property`	No	mode=property — transactions at the location
`valuation`	No	mode=valuation — price estimate
`comparables`	No	mode=comparables — aggregated comp stats
`quality_score`	Yes
`location_resolved`	Yes

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds significant behavioral context: data source details (DVF, UK Land Registry), SLA (≤25s p95), cache TTL (24h), quality scoring, status levels, and that no env vars are required. This enriches the agent's understanding beyond annotations.

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

Conciseness4/5

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

The description is lengthy but well-structured with bullet points and clear sections (modes, data sources, SLA, etc.). It is front-loaded with the core value. While dense, every sentence adds value; minor reduction would improve conciseness.

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?

Given the tool's complexity (9 parameters, nested objects, 4 modes, multiple data sources, quality scoring), the description is very complete. It covers data sources, SLA, caching, target users, and status outcomes. An output schema exists, so return values are not needed in the description.

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?

Schema coverage is 100%, so baseline is 3. The description adds meaning by detailing each mode's purpose and linking them to the mode parameter, and by providing context like default radius (500m) and surface tolerances (±20%). This goes slightly beyond what the schema alone provides.

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 clearly states it is a real estate intelligence aggregator with specific datasets (French DVF, UK Land Registry) and four distinct modes (property, comparables, market, valuation). This provides a specific verb+resource and distinguishes it from sibling tools, which are largely unrelated or different in scope.

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

Usage Guidelines4/5

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

The description explains the four modes and their use cases (e.g., 'property — full transaction history for a specific address') and mentions the ICP (target users). However, it does not explicitly state when NOT to use this tool or provide comparisons to alternative tools among siblings.

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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