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Find Property Comparables

find_property_comparables
Read-only

Find comparable properties for valuation - the #1 tool for real estate agents.

REQUIRED parameters:

  • latitude, longitude: Target property coordinates

  • type_local: "Maison" or "Appartement"

  • surface_min, surface_max: Surface range in m² (typically ±20% of target)

Optional:

  • pieces: Number of rooms (±1 tolerance applied)

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

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

  • max_age_months: Transaction age limit (default: 18, same as estimate_property_value; max: 36) When fewer than 3 comps match, the search first extends to 36 months at the same radius, then to 2 km.

  • limit: Max comparables (default: 10, max: 20)

Returns:

  • Comparable properties with similarity scores (0-1)

  • Distance from target, age of transaction

  • Adjustment suggestions (e.g., "+2% for 10m² larger")

  • Valuation estimate with confidence level, plus a data-calibrated 50% interval (interval.*) and traceable confidence_factors — see estimate_property_value's description for the full field reference

Example: Find comps for a 75m² apartment near Tour Eiffel: {latitude: 48.858, longitude: 2.294, type_local: "Appartement", surface_min: 60, surface_max: 90}

Cost: 10 credits per call

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum comparables to return (default: 10, max: 20)
piecesNoNumber of rooms (optional, ±1 tolerance used)
communeNoCommune fallback when GPS comparables are sparse
latitudeYesLatitude of the target property (required)
radius_mNoSearch radius in meters (default: 500, max: 2000)
longitudeYesLongitude of the target property (required)
type_localYesProperty type (required for matching)
code_postalNoPostal code fallback when GPS comparables are sparse
surface_maxYesMaximum surface in m² (typically target surface + 20%)
surface_minYesMinimum surface in m² (typically target surface - 20%)
max_age_monthsNoMaximum transaction age in months (default: 18, max: 36)
exclude_bulk_salesNoExclude bulk sales (default: true)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • removedInput schema / properties / max_age_months / default
      Removed value: -12
    • changedInput schema / properties / max_age_months / description
      Previous value: -"Maximum transaction age in months (default: 12, max: 36)"New value: +"Maximum transaction age in months (default: 18, max: 36)"
  2. First observed

TDQS

A4.1/5.0
Behavior5/5

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

Beyond the readOnly/openWorld annotations, the description discloses the sparse-result fallback (extend to 36 months, then 2 km), the ±1 pieces tolerance, the ±20% surface convention, the cost (10 credits per call), and the exact return payload. These are meaningful behavioral traits an agent cannot derive from annotations or schema alone.

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?

Front-loaded purpose followed by clear Required/Optional/Returns/Example sections makes it scannable for a 12-parameter tool. It is somewhat long and there is mild duplication with the schema (defaults, radius, limit) plus the throwaway '#1 tool' line, but the structure earns its length.

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?

With no output schema, the description correctly documents the return shape (similarity scores, distance/age, adjustment suggestions, estimate with confidence interval) and gives a concrete worked example. For a complex, high-parameter tool, nothing critical is left to guesswork.

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 the baseline is 3, but the description adds real semantic context: 'typically ±20% of target' framing for the surface bounds, the ±1 room tolerance, and the note that max_age_months matches estimate_property_value's default and drives the widening logic. A few items (radius default, limit cap) merely echo the schema.

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

Purpose4/5

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

States a specific verb+resource ('Find comparable properties for valuation'), which an agent can clearly separate from sibling tools like estimate_property_value or search_property_transactions. It does not explicitly contrast its role against those siblings, and the '#1 tool for real estate agents' tagline is marketing fluff, but the core purpose is unambiguous.

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 lays out required vs optional inputs and operational fallback behavior, so usage is implied. However, it never says when to reach for this tool over estimate_property_value (which is only referenced for a shared field definition) or when a plain property search suffices, leaving the when-to-use decision to inference.

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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