Aggregate Promo Discounts (Messaging)

messagingmcp-aggregate_promos

Compute a statistic over promo discount depth, deduped by promo_id.

metric defaults to 'promo_depth' — the only aggregatable field — so it can be omitted. promo_depth_type is required (depths are unitless integers — without unit pinning the tool would mix percentages with currency amounts). Multi-buy promotions are EXCLUDED from every aggregate: their depth carries a third meaning (qualifying-item %, null for pure BOGO), so 'percentage' aggregates percentage_discount promos only and 'flat' aggregates flat_discount only. Returns a scalar value plus n_promos and n_observed (how many distinct promos contributed, and how many of those have a non-inferred row). With group_by or interval, returns a buckets array of those same shapes.

Use for "average % off", "deepest discount", "median flat amount". For counts or row-level retrieval use count_promos / list_promos.

Parameters

Name	Type	Required	Constraints	Description
`statistic`	`string`	yes	—	Statistic to compute over distinct promotions' depth.
`promo_depth_type`	`string`	yes	—	REQUIRED. Pins the unit. 'percentage' for % off, 'flat' for currency-amount promos. Without it, percentages and currency amounts would be averaged together into a meaningless number.
`metric`	`string`	no	default: `"promo_depth"`	Field to aggregate. Only 'promo_depth' (numeric discount value) exists, so the param can be omitted.
`promo_currency`	`string \| null`	no	default: `null`	ISO 4217 code (e.g. GBP, USD). REQUIRED when promo_depth_type='flat' — different currencies must not be averaged. Must be omitted for 'percentage' (rejected if supplied — percentages have no currency; use `regions` to scope a market).
`retailers`	`string[] \| null`	no	default: `null`	Retailer slugs to filter by, e.g. ['zara', 'h-m']. None = all; matches any listed retailer. Call `list_retailers` and match the requested retailer against its `retailer_name`, then pass the row's `retailer` slug and a region it covers exactly as reported there. Slugs match exactly, so a display name or wrong case matches nothing.
`regions`	`string[] \| null`	no	default: `null`	Regions to filter by, e.g. ['UK', 'US']. None = all; matches any listed region. `list_retailers` reports the exact region values for each retailer; use those, since regions match exactly.
`start_date`	`string \| null`	no	default: `null`	Inclusive start of the active-date window (YYYY-MM-DD).
`end_date`	`string \| null`	no	default: `null`	Inclusive end of the active-date window (YYYY-MM-DD).
`min_depth`	`integer \| null`	no	default: `null`	Optional minimum promo_depth pre-filter.
`max_depth`	`integer \| null`	no	default: `null`	Optional maximum promo_depth pre-filter.
`has_code`	`boolean \| null`	no	default: `null`	True = only promos with a code, False = only without, null = no filter.
`include_inferred`	`boolean`	no	default: `true`	Include inferred promotions (default True). False = observed only.
`group_by`	`string \| null`	no	default: `null`	Optional categorical bucketing. Mutually exclusive with `interval`. 'shoot' groups by source image to surface co-promoted bundles.
`interval`	`string \| null`	no	default: `null`	Optional temporal bucketing on promotion_date. Exclusive with group_by. Rows are limited to [start_date, end_date], but bucket keys are calendar-aligned (week/month starts), so an edge bucket may be keyed before start_date and its statistic covers only the in-window part of that period.

Returns

On success: the stat block (or {"buckets": [...]}), with filter_echo.
On failure: {"error": str}.

Try it

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Code examples

curl
TypeScript
Python

curl -s https://mcp.edited.com/mcp \
  -H "Content-Type: application/json" \
  -H "Accept: application/json, text/event-stream" \
  -H "MCP-Protocol-Version: 2025-06-18" \
  -H "x-api-key: $MCP_API_KEY" \
  -d '{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "messagingmcp-aggregate_promos",
    "arguments": {
      "statistic": "example",
      "promo_depth_type": "example",
      "metric": "promo_depth",
      "promo_currency": null,
      "retailers": null,
      "regions": null,
      "start_date": null,
      "end_date": null,
      "min_depth": null,
      "max_depth": null,
      "has_code": null,
      "include_inferred": true,
      "group_by": null,
      "interval": null
    }
  }
}'

import { Client } from "@modelcontextprotocol/sdk/client/index.js";
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp.js";

const client = new Client({ name: "demo", version: "1.0.0" }, { capabilities: {} });
await client.connect(
  new StreamableHTTPClientTransport(new URL("https://mcp.edited.com/mcp"), {
    requestInit: { headers: { "x-api-key": process.env.MCP_API_KEY ?? "" } },
  }),
);

const result = await client.callTool({
  name: "messagingmcp-aggregate_promos",
  arguments: {
    "statistic": "example",
    "promo_depth_type": "example",
    "metric": "promo_depth",
    "promo_currency": null,
    "retailers": null,
    "regions": null,
    "start_date": null,
    "end_date": null,
    "min_depth": null,
    "max_depth": null,
    "has_code": null,
    "include_inferred": true,
    "group_by": null,
    "interval": null
  },
});

import os
from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client

headers = {"x-api-key": os.environ["MCP_API_KEY"]}
async with streamablehttp_client("https://mcp.edited.com/mcp", headers=headers) as (read, write, _):
    async with ClientSession(read, write) as session:
        await session.initialize()
        result = await session.call_tool(
            "messagingmcp-aggregate_promos",
            {"statistic": "example", "promo_depth_type": "example", "metric": "promo_depth", "promo_currency": None, "retailers": None, "regions": None, "start_date": None, "end_date": None, "min_depth": None, "max_depth": None, "has_code": None, "include_inferred": True, "group_by": None, "interval": None},
        )

Input schema

{
  "type": "object",
  "properties": {
    "statistic": {
      "description": "Statistic to compute over distinct promotions' depth.",
      "enum": [
        "mean",
        "median",
        "min",
        "max"
      ],
      "type": "string"
    },
    "promo_depth_type": {
      "description": "REQUIRED. Pins the unit. 'percentage' for % off, 'flat' for currency-amount promos. Without it, percentages and currency amounts would be averaged together into a meaningless number.",
      "enum": [
        "percentage",
        "flat"
      ],
      "type": "string"
    },
    "metric": {
      "const": "promo_depth",
      "default": "promo_depth",
      "description": "Field to aggregate. Only 'promo_depth' (numeric discount value) exists, so the param can be omitted.",
      "type": "string"
    },
    "promo_currency": {
      "anyOf": [
        {
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "ISO 4217 code (e.g. GBP, USD). REQUIRED when promo_depth_type='flat' — different currencies must not be averaged. Must be omitted for 'percentage' (rejected if supplied — percentages have no currency; use `regions` to scope a market)."
    },
    "retailers": {
      "anyOf": [
        {
          "items": {
            "type": "string"
          },
          "type": "array"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Retailer slugs to filter by, e.g. ['zara', 'h-m']. None = all; matches any listed retailer. Call `list_retailers` and match the requested retailer against its `retailer_name`, then pass the row's `retailer` slug and a region it covers exactly as reported there. Slugs match exactly, so a display name or wrong case matches nothing."
    },
    "regions": {
      "anyOf": [
        {
          "items": {
            "type": "string"
          },
          "type": "array"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Regions to filter by, e.g. ['UK', 'US']. None = all; matches any listed region. `list_retailers` reports the exact region values for each retailer; use those, since regions match exactly."
    },
    "start_date": {
      "anyOf": [
        {
          "format": "date",
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Inclusive start of the active-date window (YYYY-MM-DD)."
    },
    "end_date": {
      "anyOf": [
        {
          "format": "date",
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Inclusive end of the active-date window (YYYY-MM-DD)."
    },
    "min_depth": {
      "anyOf": [
        {
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Optional minimum promo_depth pre-filter."
    },
    "max_depth": {
      "anyOf": [
        {
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Optional maximum promo_depth pre-filter."
    },
    "has_code": {
      "anyOf": [
        {
          "type": "boolean"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "True = only promos with a code, False = only without, null = no filter."
    },
    "include_inferred": {
      "default": true,
      "description": "Include inferred promotions (default True). False = observed only.",
      "type": "boolean"
    },
    "group_by": {
      "anyOf": [
        {
          "enum": [
            "retailer",
            "promo_type",
            "region",
            "shoot"
          ],
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Optional categorical bucketing. Mutually exclusive with `interval`. 'shoot' groups by source image to surface co-promoted bundles."
    },
    "interval": {
      "anyOf": [
        {
          "enum": [
            "day",
            "week",
            "month"
          ],
          "type": "string"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Optional temporal bucketing on promotion_date. Exclusive with group_by. Rows are limited to [start_date, end_date], but bucket keys are calendar-aligned (week/month starts), so an edge bucket may be keyed before start_date and its statistic covers only the in-window part of that period."
    }
  },
  "required": [
    "statistic",
    "promo_depth_type"
  ]
}

Parameters​

Returns​

Try it​

Code examples​

Input schema​

Parameters

Returns

Try it

Code examples

Input schema