ExitValue.ai M&A Multiples

Median EV/EBITDA and EV/Revenue multiples by industry and deal size from real M&A transactions.

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品質與安全性

A
說明品質
100%
結構描述完整度
83%
命名品質
95%
汙染風險
100%
權限相符程度
100%
協定合規性
100%

根據工具定義與協定合規性的自動化分析。

上下文成本

~1,407Token(工具定義)
~1.8 KB典型回應大小
中等的注意力影響(128k 上下文的 1.10%)

這是每次將伺服器的工具載入模型上下文時所消耗的約略 token 數量。數量越高,可用於其他工作的注意力就越少。

安裝

一鍵安裝

將以下內容加入你的 `claude_desktop_config.json` 檔案:

{
  "mcpServers": {
    "ma-multiples": {
      "url": "https://exitvalue.ai/api/mcp"
    }
  }
}

遠端端點

https://exitvalue.ai/api/mcpstreamable-http

它能做什麼

工具清單

工具(4)

🟢 唯讀🟡 寫入🔴 刪除⚪ 未知
🟢list_industries

Read-only. Lists every industry and sub-vertical ExitValue.ai tracks, with their ids and display names, and flags which sub-verticals have published multiples (has_published_multiples). Takes no input. Use it first when you are unsure of an id; the other tools accept these ids or plain-English names.

輸入結構描述

{
  "type": "object",
  "properties": {},
  "required": [],
  "additionalProperties": false
}
🟢get_valuation_multiples(industry, size_bracket)

Read-only. Answers 'what multiple does a business in industry X sell for?' for ONE industry. Returns median EV/EBITDA and EV/Revenue multiples with the middle 50% (25th-75th percentile) and deal count n, broken down by enterprise-value bracket (disclosed deals 2018-2026, n>=10 per cell) plus an all-deal-sizes figure (n>=5). Figures below those thresholds are withheld, never estimated. Passing an industry group id (e.g. 'home-services') returns one all-sizes median per sub-vertical. Use compare_industries to put several industries side by side and get_multiples_trend for year-by-year history. Population medians only: it does not value a specific business.

輸入結構描述

{
  "type": "object",
  "properties": {
    "industry": {
      "type": "string",
      "description": "Sub-vertical id from list_industries (e.g. 'hvac', 'dental-practice'), or a plain-English name ('HVAC', 'dentist', 'plumber', 'MSP'); plain names are mapped to the closest tracked category and the answer says which one was used. An industry group id such as 'healthcare' or 'home-services' is also accepted."
    },
    "size_bracket": {
      "type": "string",
      "enum": [
        "under_5m_ev",
        "5m_25m_ev",
        "25m_100m_ev",
        "100m_500m_ev",
        "over_500m_ev"
      ],
      "description": "Optional enterprise-value (deal size) bracket: under_5m_ev = under $5M, 5m_25m_ev = $5M-$25M, 25m_100m_ev = $25M-$100M, 100m_500m_ev = $100M-$500M, over_500m_ev = $500M and up. Omit to get every published bracket."
    }
  },
  "required": [
    "industry"
  ],
  "additionalProperties": false
}
🟢compare_industries(industries, size_bracket, metric)

Read-only. Puts 2-6 industries side by side for one metric: median multiple, middle 50% (25th-75th percentile) and deal count n per industry, using the same data, name matching and thresholds as get_valuation_multiples. Without size_bracket it compares all-deal-size medians (n>=5); with size_bracket it compares that enterprise-value bracket (n>=10). Rows below the threshold are marked INSUFFICIENT DATA rather than estimated; unmatched names and industry-group ids are flagged per row. Use it for 'which sells for more, X or Y?' questions; use get_valuation_multiples for the full breakdown of one industry.

輸入結構描述

{
  "type": "object",
  "properties": {
    "industries": {
      "type": "array",
      "minItems": 2,
      "maxItems": 6,
      "items": {
        "type": "string",
        "description": "Sub-vertical id from list_industries (e.g. 'hvac', 'dental-practice'), or a plain-English name ('HVAC', 'dentist', 'plumber', 'MSP'); plain names are mapped to the closest tracked category and the answer says which one was used."
      },
      "description": "2 to 6 sub-vertical ids or plain-English industry names to compare, e.g. ['dental-practice', 'veterinary-practice', 'physical therapy']."
    },
    "size_bracket": {
      "type": "string",
      "enum": [
        "under_5m_ev",
        "5m_25m_ev",
        "25m_100m_ev",
        "100m_500m_ev",
        "over_500m_ev"
      ],
      "description": "Optional enterprise-value (deal size) bracket: under_5m_ev = under $5M, 5m_25m_ev = $5M-$25M, 25m_100m_ev = $25M-$100M, 100m_500m_ev = $100M-$500M, over_500m_ev = $500M and up. Omit to compare all deal sizes pooled."
    },
    "metric": {
      "type": "string",
      "enum": [
        "ev_ebitda",
        "ev_revenue"
      ],
      "default": "ev_ebitda",
      "description": "Multiple to compare: ev_ebitda (enterprise value / EBITDA, default) or ev_revenue (enterprise value / revenue)."
    }
  },
  "required": [
    "industries"
  ],
  "additionalProperties": false
}
🟢get_multiples_trend(industry, metric)

Read-only. Year-by-year history for ONE sub-vertical: for each deal year 2018-2026, the median EV/EBITDA and/or EV/Revenue multiple with middle 50% (25th-75th percentile), deal count n and median deal value. Only years with at least 10 disclosed deals for a metric are shown; missing years are listed as gaps and never interpolated, so many sub-verticals have few or no years. Use it for 'are multiples rising or falling for X?' questions; use get_valuation_multiples for pooled current figures by deal size.

輸入結構描述

{
  "type": "object",
  "properties": {
    "industry": {
      "type": "string",
      "description": "Sub-vertical id from list_industries (e.g. 'hvac', 'dental-practice'), or a plain-English name ('HVAC', 'dentist', 'plumber', 'MSP'); plain names are mapped to the closest tracked category and the answer says which one was used."
    },
    "metric": {
      "type": "string",
      "enum": [
        "ev_ebitda",
        "ev_revenue"
      ],
      "description": "Optional: ev_ebitda or ev_revenue. Omit to show both where published."
    }
  },
  "required": [
    "industry"
  ],
  "additionalProperties": false
}

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