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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

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

A4.1/5.0
Behavior4/5

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

Goes beyond the readOnlyHint/openWorldHint annotations by disclosing a real behavioral constraint (radius_m > 1000m is silently restricted to the last 12 months) and a cost of 5 credits per call. It also lists the return fields, though it doesn't say how results are ordered or capped.

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-loads the purpose, then uses labeled sections (REQUIRED, Examples, Returns, Note, Cost) that are easy to scan. Three examples are arguably one more than needed, but each illustrates a distinct filter mode, so little is wasted.

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?

For a 14-parameter tool with no output schema, the description supplies the missing pieces: the required-filter rule, the returned metric set, the radius time-window caveat, and the per-call cost. An agent can invoke this correctly without opening the schema.

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 genuine semantics: the OR-relationship among location filters, the latitude+longitude pairing, the uppercase 'PARIS 11' convention for commune, and the 500m default radius. It adds real value over the schema descriptions without restating them.

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 and resource plus the exact output metrics (avg/median prices, price per m², surface areas), which lets an agent distinguish it from sibling analytics tools like analyze_price_trends or get_market_overview. It stops short of explicitly naming which sibling to use instead for trend or overview questions.

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 states a hard precondition ('You MUST provide at least one location filter') and enumerates the four acceptable filter forms, then gives worked examples for each. It does not state when to prefer a sibling tool (e.g. compare_locations or estimate_property_value), so no exclusions are covered.

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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