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Estimate Property Value

estimate_property_value
Read-only

Get an automated valuation (AVM) for a property — low / mid / high price range.

Use find_property_comparables instead if you want the raw comparable transactions with similarity scores. estimate_property_value returns only the AVM summary (low/mid/high range).

REQUIRED: latitude, longitude, surface

Optional:

  • type_local: "Maison" or "Appartement" (default: Appartement)

  • pieces: Number of rooms

  • code_postal, commune: Optional administrative fallback when GPS comparables are sparse

  • radius_m: Search radius (100–2000m, default: 500)

  • max_age_months: Max transaction age (1–24, default: 18)

Returns:

  • price_range: { low, mid, high } in euros

  • price_per_m2: { median, used_surface }

  • confidence: "high", "medium" or "low". It reflects comparable count and average similarity; it is not a calibrated probability of accuracy.

  • based_on_count: number of comparables used

  • interval: { method, coverage_label, basis, cohort, cohort_eligible_n, ratio_low, ratio_high, contract_version, calibration_generated_at } — how price_range was derived.

  • confidence_factors: { comparable_count, average_similarity, fallback_used, positive_factors[], limiting_factors[] } — traceable inputs to confidence.

Returns estimate: null with a message if fewer than 3 comparables are found. Tip: increase radius_m or max_age_months to get more data.

Note: the low/high range is a data-calibrated 50% interquartile interval (P25/P75 of observed-price / published-mid, backtest-measured on 2,000 masked DVF sales), not a fixed percentage. Its width is driven first by how much the retained comparables agree on price/m² — the dispersion cohort, (P75-P25) / median, e.g. interval.cohort = "dispersion=<15%" — then by comparable density, property type and surface band, and it widens to the global cohort when a segment lacks enough measured data (interval.basis). Agreeing or contradicting comparables also surface as confidence_factors comparables_agree / comparables_disagree. The mid value is unaffected by any of this. DVF data covers 2014–2025 (annual cadence). Use max_age_months ≥ 12 for reliable results.

Cost: 10 credits per call

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
piecesNoNumber of rooms (±1 tolerance applied to comparables)
communeNoCommune fallback when GPS comparables are sparse
surfaceYesSurface area in m²
latitudeYesGPS latitude of the target property
radius_mNoSearch radius in meters (default: 500)
longitudeYesGPS longitude of the target property
type_localNoProperty type (default: Appartement)
code_postalNoPostal code fallback when GPS comparables are sparse
max_age_monthsNoMax age of comparable transactions in months (default: 18 — DVF updates semi-annually)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Far exceeds the annotations (readOnlyHint/openWorldHint). It discloses the cost (10 credits), the null-return threshold (<3 comparables), the confidence semantics ('not a calibrated probability'), the interval methodology (backtest on 2,000 masked DVF sales, P25/P75, cohort-based width), and DVF coverage 2014–2025. Nothing here contradicts annotations.

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

Conciseness3/5

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

Front-loaded well (purpose first, then routing, then required params). However, the interval/cohort methodology paragraph is dense and lengthy, and largely restates what the return object would convey. It earns partial credit but is more verbose than an agent needs to choose and call the tool.

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

Completeness4/5

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

For a read-only AVM tool with no output schema, the description covers inputs, outputs, failure mode, cost, and confidence caveats thoroughly. Left slightly incomplete on error semantics beyond the <3 comparables case and any rate-limit/permission context, but overall strongly complete.

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 already 100%, so baseline is 3. The description adds meaning beyond the schema: which params are REQUIRED, defaults (Appartement, 500m, 18mo), the fallback purpose of code_postal/commune, and the '±1 tolerance' context for pieces. It does not add syntax beyond the schema but does add decision-relevant context.

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?

States a specific verb and resource ('Get an automated valuation (AVM) for a property') and immediately distinguishes itself from the closest sibling, find_property_comparables, by contrasting outputs (AVM summary vs raw comparable transactions). An agent can route correctly without opening either schema.

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?

Explicitly names the alternative tool and the condition that selects it (raw comparables with similarity scores). It also gives a recovery tip ('increase radius_m or max_age_months') and a data-recency condition ('use max_age_months ≥ 12'). It lacks a clear 'do not use when' for the many other analysis siblings, so it falls just short of 5.

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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