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Analyze Market Statistics

analyze_market_statistics
Read-only

Get market statistics: avg/median prices, price per m², surface areas.

REQUIRED: You MUST provide at least one location filter:

  • code_postal (e.g., "75011")

  • commune (e.g., "PARIS 11" - uppercase; Paris uses "PARIS 01"–"PARIS 20")

  • code_departement (e.g., "75")

  • latitude + longitude for radius search

Examples:

  • Paris 11e market: {commune: "PARIS 11", type_local: "Appartement"}

  • 500m radius: {latitude: 48.8566, longitude: 2.3522, radius_m: 500}

  • Department stats: {code_departement: "69", type_local: "Maison"}

Returns: count, price (min/max/avg/median), surface stats, price_per_m2. The figures leave out the scope's lowest and highest 1% price per m² (outliers_excluded, 20+ sales); count is every matching sale.

Note: radius_m > 1000m is automatically restricted to the last 12 months of data.

Cost: 5 credits per call

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
communeNoCity/commune name (e.g., 'Paris', 'Lyon')
date_finNoEnd date (YYYY-MM-DD)
latitudeNoLatitude for radius search (requires longitude)
radius_mNoRadius in meters for location search (default: 500)
longitudeNoLongitude for radius search (requires latitude)
date_debutNoStart date (YYYY-MM-DD)
type_localNoProperty type
code_postalNoPostal code (e.g., '75001')
exclude_vefaNoExclude VEFA (new-build, vente en l'état futur d'achèvement) sales (default: true — keeps 'ancien' results from double-counting new-build activity)
include_terrainNoInclude terrain-only transactions in search results (default: false)
code_departementNoDepartment code (e.g., '73' for Savoie, '83' for Var)
use_original_typeNoUse original type_local instead of smart computed_type_local (default: false)
exclude_bulk_salesNoExclude bulk sales and aggregated transactions (default: true for clean market data)
include_pouvoir_achatNoAdd a price-to-income ratio field (INSEE Filosofi × DVF). Ignored on radius searches.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • removedInput schema / properties / exclude_outliers
      Removed value: -{
-  "default": true,
-  "description": "Exclude price outliers (default: true for clean market data)",
-  "type": "boolean"
-}
  2. First observed

TDQS

A4.3/5.0
Behavior5/5

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

Beyond the readOnlyHint annotation, the description discloses non-obvious data behavior: the 1% price-per-m² outlier trim (with the 20+ sales threshold), that count reflects every matching sale, that radius >1000m silently restricts to 12 months, and the 5-credit cost. These are the kind of quirks an agent could not infer from the schema and would otherwise get wrong.

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

Conciseness5/5

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

Front-loads purpose, then the REQUIRED constraint, then scannable examples, then returns, caveats, and cost — a natural decision order with no filler. The formatting (bullets, inline examples) makes a 14-parameter tool skimmable.

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 14-param, no-output-schema tool this covers purpose, preconditions, output fields, statistical caveats, and cost, which is nearly everything an agent needs. Minor gaps remain: date-range defaults/filtering and how overlapping location filters combine are left to the schema alone.

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 baseline is 3, but the description adds real meaning the schema lacks: the uppercase 'PARIS 11' commune convention with the PARIS 01–PARIS 20 note, the latitude+longitude pairing for radius search, and cross-parameter example combinations. It does not explain the filter-interaction semantics (e.g., combining commune with code_postal).

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?

The opening sentence gives a specific verb and resource plus concrete output metrics (avg/median prices, price per m², surface areas), so an agent knows exactly what this computes. However, it never distinguishes itself from close siblings like get_market_overview, analyze_market_activity, or get_zonal_price_distribution, which all sound like market statistics at a glance.

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?

It states a hard precondition (at least one location filter) and enumerates the four accepted location modes, which is strong operational guidance. It stops short of saying when-not to use it or naming an alternative tool for trend/activity analysis, so the sibling-routing guidance is missing.

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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