zRev AI

ai.zrev由 zrev.ai 發布官方發布者v1.0.0更新於 Oct 24, 2026

Grade any llms.txt, cold-read a homepage, run a B2B GTM ROI model, get a GTM diligence checklist.

已驗證Streamable HTTP可網頁執行商業與電商

概覽

AI 產生的概覽

讓助理查詢 zRev 的服務與成效資料、估算 GTM ROI、評分 llms.txt、判讀公司首頁並留下聯絡資訊。

可以做什麼
提供 zRev 的公司簡介、典型成效數據、60 天合作時程與 GTM 盡職調查清單(47 題、十個面向),皆為唯讀靜態內容。另有 estimate_roi 依單一公司數字推算年度影響,grade_llms_txt 以七項檢查為網站的 llms.txt 評分,cold_read 僅依公開首頁可見文字概述該公司。book_call 只回傳預約連結,leave_contact 會寫入一筆潛在客戶紀錄。
適用情境
當使用者詢問 zRev 是什麼、服務內容、合作流程或預期成效,或想以自家數字估算 GTM ROI、檢查網站的 llms.txt、快速判讀某公司首頁,或索取 GTM 盡職調查清單時適用。
執行需求
遠端 HTTP 端點,無需安裝套件;未宣告驗證、環境變數或標頭。grade_llms_txt 與 cold_read 需連外抓取指定網域的公開頁面。
安裝前請注意
leave_contact 會將電子郵件及選填的姓名、公司、備註寫入 zRev 端的潛在客戶紀錄,僅在當事人同意時使用。estimate_roi 的結果為模型推估,成效數字屬典型值並附帶來源說明。grade_llms_txt 與 cold_read 會向第三方網站發出請求。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 zRev AI,將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Web executable,透過 Streamable HTTP。 遠端服務在工作區中設定後即可從網頁執行環境執行。

交給代理程式安裝

把這段提示詞貼上到 Claude Code、Codex、Cursor 等代理程式,或 SourceWeft 對話中。代理程式會讀取該伺服器的安裝指南,先向你展示發布者、端點和登入方式,經你同意後再把它新增到你的客戶端。金鑰不會寫進任何命令。

閱讀 https://sourceweft.com/mcp/ai.zrev%2Fzrev/install.md,並按其中說明把這個 MCP 伺服器接入我的客戶端。修改任何設定之前,先告訴我它的發布者、端點和登入方式,等我確認。不要把 API 金鑰寫進命令或我會提交的檔案;問我金鑰應從哪裡取得。

檢視代理程式所依據的安裝指南

其他 MCP 客戶端

把它新增到你客戶端的 mcpServers 設定中。

{
  "mcpServers": {
    "zrev-ai": {
      "type": "http",
      "url": "https://go.zrev.ai/mcp"
    }
  }
}

工具

9

about_zrev

About zRev

Returns a plain-text profile of zRev AI: what the firm does, the companies it is the best fit for, who it is not for, its six service lines with a link to each, and contact details. Use this first when a user asks who zRev is, what it offers, or whether it suits their company. Read-only, static content, no arguments, no authentication, no rate limit.

無需輸入參數。

get_benchmarks

Typical engagement results

Returns zRev's typical engagement results as plain text: 2x pipeline velocity, 20% lower customer acquisition cost, 10 hours back per rep per week, 60% of GTM busywork automated, results that start inside 30 days and mature by 60. The response always ends with the provenance caveat: these are typical results benchmarked against comparable AI-powered GTM implementations and they vary by stack, data quality and adoption, so present them as typical, not guaranteed. Use when a user asks what results to expect. For an estimate on a specific company's numbers use estimate_roi instead. Read-only, static content, no arguments, no authentication, no rate limit.

無需輸入參數。

get_engagement_timeline

The 60-day engagement, phase by phase

Returns the phases of a standard 60-day zRev engagement as plain text: what happens in each phase, what is delivered, and when first results appear. Use when a user asks how an engagement works, how long it takes, or what they would receive and when. Read-only, static content, no arguments, no authentication, no rate limit.

無需輸入參數。

estimate_roi

Estimate the ROI of AI across a GTM motion

Runs zRev's ROI model on one company's numbers and returns, as plain text, the projected annual impact in USD split into pipeline lift, customer acquisition cost savings and the value of hours returned to the team, followed by the model's assumptions and a link to the interactive calculator preset to the same inputs. Use when a user wants a number for their own company; all six inputs are required, so ask for any that are missing rather than guessing. The output is an estimate from a fixed model, not a forecast or a quote. Pure calculation: nothing is stored, no external calls, no authentication, no rate limit.

輸入參數arr必填cac必填deal_size必填team_size必填monthly_leads必填close_rate_pct必填

grade_llms_txt

Grade a site's llms.txt

Fetches https://<domain>/llms.txt (the file that tells AI systems what a site contains) and grades it with seven deterministic checks. Returns a 0 to 100 score and the pass or miss result of each check as plain text. If the site has no llms.txt the response says so and links to zRev's free generator. Use when a user asks whether a site is readable by AI assistants or wants their llms.txt reviewed. Makes one outbound HTTP request to the public domain you pass; stores nothing about it. No authentication. Shares a fair-use limit per IP address with cold_read, and returns a plain-text notice when that limit is reached.

輸入參數domain必填

cold_read

Cold-read a homepage

Fetches a company's public homepage and returns, in two or three sentences, what the company appears to do and who it serves, based only on the visible text of that page and with no outside knowledge. Anything missing from the answer is missing from the homepage, which is the point: it shows what an AI assistant would tell a buyer about that company. Use when a user asks how their site, or a competitor's, reads to an AI. The summary is model-generated and can be wrong where the page is vague. Makes one outbound HTTP request to the public domain you pass; stores nothing about it. No authentication. Shares a fair-use limit per IP address with grade_llms_txt, and returns a plain-text notice when that limit is reached.

輸入參數domain必填

gtm_diligence_checklist

GTM due diligence checklist

Returns zRev's go-to-market due diligence checklist for investors and acquirers: 47 questions across ten dimensions, each with the data-room artifact that answers it and the red flag to watch for. Call with no arguments to get the list of ten dimensions; call with a dimension number from 1 to 10, or a keyword such as churn, pipeline or marketing, to get that dimension's questions in full. Use when a user is assessing a company's revenue engine before an investment or acquisition. Read-only reference content, no authentication, no rate limit.

輸入參數dimension

book_call

How to book an intro call

Returns the link and instructions for booking a free 30-minute intro call with zRev's founder, plus what the call covers. It does not book anything: it only returns the scheduling link for the human to open themselves. Use when a user says they want to talk to zRev. If they would rather be contacted, use leave_contact instead. Read-only, static content, no arguments, no authentication, no rate limit.

無需輸入參數。

leave_contact

Ask zRev to get in touch

Records an email address, with optional name, company and note, so that zRev's founder can reach out, and returns a plain-text confirmation. This is the only tool here that writes data: it creates one lead record on zRev's side and triggers no email to the address given. Use only when the human you are assisting has explicitly asked to be contacted by zRev; never call it speculatively or with an address the user has not given you for this purpose. Submitting the same email again updates the existing record rather than creating a duplicate. No authentication. Rate-limited per IP address.

輸入參數namenoteemail必填company

版本歷史

1
  1. v1.0.0最新Sep 17, 2026

評分

已滿足適用檢查項的 72%

該評分衡量此收錄條目的完整程度,依據註冊中心條目、端點與公開來源計算得出。它不是對伺服器本身的評價,也不表示伺服器安全或目前可用:可連通性是帶日期的檢查結果,評分也不包含安裝量或使用者評價。

  • 近 30 天內可連通必需25 分
  • 已宣告認證方式必需10 分
  • 已列出工具必需15 分
  • 描述必需15 分
  • 圖示10 分

    請在註冊中心條目中發布圖示。

  • 文件15 分

    請宣告包含 README 的 GitHub 儲存庫,或在 OAuth 受保護資源後設資料中提供 resource_documentation。

  • 授權條款不適用

    託管服務按其提供方的條款使用,因此這裡不要求授權條款。此檢查項不計入百分比。