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Analyze Price Trends

analyze_price_trends
Read-only

Analyze price evolution over time - essential for market timing and investment decisions.

REQUIRED: At least one location filter:

  • code_postal, commune (e.g., "PARIS 11"), or code_departement

  • No coordinates or radius: for a point, pass its code_postal or commune (find_property_comparables covers a radius)

Optional:

  • type_local: "Maison", "Appartement", "Terrain", "Local commercial"

  • granularity: "month", "quarter" (default), or "year"

  • date_debut/date_fin: Date range (default: last 5 years)

Returns:

  • Time series with median/avg prices per period

  • Year-over-year changes (%)

  • Overall trend: "increasing", "decreasing", or "stable" (±5%), on the median price per m² (summary.change_basis)

  • Total change over the period

Example: Paris 11e apartment price trends: {commune: "PARIS 11", type_local: "Appartement", granularity: "quarter"}

Note: DVF data covers 2014–2025 (annual cadence). Use date windows ≥ 12 months for reliable results.

Cost: 10 credits per call

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "analyze_price_trends"
  }
}

Input Schema

NameRequiredDescriptionDefault
communeNoCity/commune name (e.g., 'PARIS')
date_finNoEnd date YYYY-MM-DD (default: today)
date_debutNoStart date YYYY-MM-DD (default: 5 years ago)
type_localNoProperty type
code_postalNoPostal code (e.g., '75001')
granularityNoTime granularity (default: quarter)quarter
exclude_vefaNo
code_departementNoDepartment code (e.g., '75')
exclude_bulk_salesNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • removedInput schema / properties / latitude
      Removed value: -{
-  "description": "Latitude for radius search",
-  "type": "number"
-}
    • removedInput schema / properties / longitude
      Removed value: -{
-  "description": "Longitude for radius search",
-  "type": "number"
-}
    • removedInput schema / properties / radius_m
      Removed value: -{
-  "description": "Radius in meters",
-  "minimum": 1,
-  "type": "number"
-}
  2. Changed1 schema field changed
    • removedInput schema / properties / exclude_outliers
      Removed value: -{
-  "default": true,
-  "type": "boolean"
-}
  3. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Annotations cover the safety profile (readOnlyHint=true, openWorldHint=false), and the description adds substantial context beyond them: cost (10 credits/call), DVF data coverage 2014–2025 with annual cadence, and the recommendation to use date windows ≥ 12 months. This is exactly the operational context an agent needs before invoking.

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-loaded purpose followed by clearly sectioned REQUIRED / Optional / Returns / Example / Note / Cost blocks. The example is concrete and each section carries distinct, load-bearing information with no redundancy.

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 carries the return-shape burden and does so: time series medians/averages, YoY changes, the trend classification with its ±5% threshold and change_basis, and total change. Combined with the data-coverage note and cost, an agent can call and interpret this tool without further information.

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 78%, and the description adds a constraint the schema does not express: the schema declares 0 required params, yet the description states at least one location filter is mandatory. It also clarifies the granularity default and the 5-year date default. It does not cover exclude_vefa or exclude_bulk_sales, leaving a small gap.

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: analyzing price evolution over time as a time series, with the use case (market timing). It differentiates from one sibling by noting find_property_comparables handles radius searches, but doesn't explicitly distinguish itself from temporal-adjacent siblings like analyze_market_activity or analyze_market_statistics.

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?

Gives strong context: at least one location filter is required, and it explicitly routes radius/coordinate queries to find_property_comparables with the reason. It lacks any when-not-to-use guidance relative to the other analyze_* siblings, but the alternative routing it does provide is explicit and actionable.

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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