ma_get_time_series_operations

Over REST: no operation in the public API description does this. MCP and REST compared.

#Description

Performs analytical time-series operations (e.g., mean, delta_pct, CAGR, cv, std, etc.) for a single geographic or analytical segment using real-estate historical data.

This tool enables focused analysis of real estate market dynamics over time — such as prices, listings, or sales — within one defined location or subset.

#Purpose

Designed for analytical scenarios such as:

• Tracking how property prices evolve in a specific area.
• Measuring growth trends (e.g., CAGR, percentage deltas).
• Evaluating volatility using standard deviation or coefficient of variation.
• Studying local market activity over a given period.

#Input (request_data)

Expects an instance of MCPTimeSeriesOperationsRequestSchema, which includes:

• segment → a single MCPTimeSeriesRequestSchema object defining the geographic or analytical subset.
• operations → list of time-series operations (TimeSeriesOperationEnum) to compute.

MCPTimeSeriesRequestSchema includes:

• custom_location_boundary: spatial boundary (circle or list of location IDs).
• data_point: metric to analyze (e.g. AVERAGE_PRICE_PER_SQM, SOLD_COUNT, NEW_LISTED_COUNT).
• date_interval: aggregation interval (WEEK, MONTH, QUARTER, or YEAR).
• date_range: start and end of the analysis period.
• Optional property filters (price, area, rooms, construction year, etc.).
• exclude_outliers: whether to exclude statistical outliers.

Analytical consistency rule: The analytical context (data_point, date_interval, business_type, etc.) must remain consistent within the request. The segment may only redefine custom_location_boundary and alias relative to the base filter.

#Operations

Supported operations from TimeSeriesOperationEnum include:

• mean — average value across the time range.
• delta_pct — percentage change between the first and last data points.
• cagr — compound annual growth rate.
• std — standard deviation.
• cv — coefficient of variation.
• (plus others defined in the enum).

#LLM Behavior

• On success → returns a single analytical result (MCPTimeSeriesUnitResponseSchema) containing computed metrics for the requested segment.
• On error → raises a ToolError with an error message.

#Example Success Response

    {
      "alias": "Madrid Center",
      "date_start": "2020-01-01",
      "date_end": "2024-01-01",
      "date_interval": "MONTH",
      "total_of_data_points": 48,
      "results": [
        {"operation": "mean", "value": 3200.5},
        {"operation": "delta_pct", "value": 12.4},
        {"operation": "cagr", "value": 0.032}
      ]
    }

#Example Error Response

ToolError: No data found for the specified segment.

#Example Use Cases

• Analyze average price per m² in a specific city or district.
• Measure sales growth over several years.
• Evaluate rental market volatility.
• Assess seasonal dynamics of listings or sales activity.

#Summary

get_time_series_operations retrieves aggregated real-estate time-series data for a defined location or segment and computes selected analytical operations. It provides a structured, quantitative summary of market trends and changes for a single region or subset.

#Parameters

#Response

#Example call

Required arguments only, with placeholder values. Your assistant fills them in from the parameters above.

{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "ma_get_time_series_operations",
    "arguments": {
      "request_data": {
        "segment": {
          "type_group": "apartment",
          "business_type": "sale",
          "alias": "…",
          "custom_location_boundary": {
            "location_ids": [
              1
            ]
          },
          "data_point": "avg_price",
          "date_interval": "week",
          "date_range": {
            "min": "2025-01-01"
          }
        }
      }
    }
  }
}

#Questions about this

• What is Area Insights?
• Which tools exist only over MCP, and which operations only over REST?
• How do I analyse how prices in an area have moved over time?