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mcp-knowledge

by io.github.getgapup

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
NameRequiredDescriptionDefault
modeYesproperty: transactions at an address | comparables: sample around a point | market: commune/neighbourhood market stats | valuation: price estimate for a given surface
asyncNoIf 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_toNoISO date YYYY-MM-DD — latest transaction date
locationYesLocation descriptor. One of: {address, city?, country?} | {lat, lon, radius_m?} | {insee_code} for FR communes.
date_fromNoISO date YYYY-MM-DD — earliest transaction date
max_resultsNoMaximum number of results to return (5–50, default 20)
surface_maxNoMaximum surface in m² (±20% tolerance applied for comparables)
surface_minNoMinimum surface in m² (±20% tolerance applied for comparables)
property_typeNoFilter by property type (default: all)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYes
marketNomode=market — commune-level market stats
statusYes
sourcesYes
propertyNomode=property — transactions at the location
valuationNomode=valuation — price estimate
comparablesNomode=comparables — aggregated comp stats
quality_scoreYes
location_resolvedYes

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral details including SLA (≤25s p95), cache TTL (24h), quality scoring mechanics, and the fact that no environment variables are required.

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 relatively long but well-structured, front-loading the core purpose and modes. Almost every sentence adds unique value, though minor condensation could improve brevity without loss of information.

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, multiple modes), the description is remarkably complete. It covers data sources, mode details, quality scoring, SLA, cache policy, and target audience. The output schema exists separately, so its absence is not a deficiency.

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 the description's burden is lower. However, it adds meaningful context beyond the schema: it explains the alternating location descriptor forms (address+city, lat+lon+radius, insee_code) and clarifies the async parameter's behavior. The mode descriptions are also richer than the enum descriptions.

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 specifies that the tool aggregates real estate intelligence from French DVF and UK Land Registry data with four distinct modes (property, comparables, market, valuation). It uniquely positions itself among siblings by focusing on real estate data.

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 provides explicit when-to-use guidance for each of the four modes and notes that all sources are free and keyless. It does not explicitly mention alternative tools or when not to use, but the mode descriptions are sufficiently clear for correct selection.

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

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

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