real_estate_intel
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 addr...
This record as markdown: /tools/io-github-getgapup-mcp-knowledge/real-estate-intel.md
What real_estate_intel does on Mcp Knowledge
AI agents call real_estate_intel to retrieve information from Mcp Knowledge without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
| Parameter | Type | Required | Description |
|---|---|---|---|
mode | string | Yes | property: transactions at an address | comparables: sample around a point | market: commune/neighbourhood market stats | valuation: price estimate for a given s |
async | boolean | — | 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 ti |
date_to | string | — | ISO date YYYY-MM-DD — latest transaction date |
location | object | Yes | Location descriptor. One of: {address, city?, country?} | {lat, lon, radius_m?} | {insee_code} for FR communes. |
date_from | string | — | ISO date YYYY-MM-DD — earliest transaction date |
max_results | number | — | Maximum number of results to return (5–50, default 20) |
surface_max | number | — | Maximum surface in m² (±20% tolerance applied for comparables) |
surface_min | number | — | Minimum surface in m² (±20% tolerance applied for comparables) |
property_type | string | — | Filter by property type (default: all) |
Parameters from the server's own tool schema.
Why real_estate_intel is rated Low
This tool exclusively reads and aggregates data from public real estate datasets (French DVF and UK Land Registry). All four modes (property, comparables, market, valuation) are retrieval and analysis operations with no side effects, writes, or destructive actions. Severity is low because misuse only risks disclosure of publicly available transaction data.
From the tool's definition 'Real estate intelligence aggregator', 'full transaction history for a specific address', 'comparables — median/std price/m²', 'market — annual price series', 'valuation — two-method estimate' — all modes retrieve and query existing public datasets
Risk signalsHigh parameter count (16 properties)
Attacks that exploit this kind of access
The rule that runs real_estate_intel safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Mcp Knowledge, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For real_estate_intel, this is the rule to start with:
real_estate_intel is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Mcp Knowledge, apply this rule, and every real_estate_intel call is checked against it from then on.
Questions about real_estate_intel
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. It is categorised as a Read tool in the Mcp Knowledge MCP Server, which means it retrieves data without modifying state.
real_estate_intel accepts 9 parameters: mode, async, date_to, location, date_from, max_results, surface_max, surface_min, property_type. Required: mode, location. The full parameter table on this page comes from the server's own tool schema.
Register the Mcp Knowledge MCP server in PolicyLayer and add a rule for real_estate_intel: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Mcp Knowledge. Nothing to install.
real_estate_intel is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the real_estate_intel rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for real_estate_intel. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
real_estate_intel is provided by the Mcp Knowledge MCP server (https://mcp.gapup.io). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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