zRev AI

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

• 连接方式: 在云端运行
• 传输方式: Streamable HTTP
• 分类: 搜索与 AI 记忆

安装

选择你使用的 AI 应用，然后复制设置。

通过 2/3 项检查 · 你的提问会经过开发者的服务器（go.zrev.ai）

claude mcp add --transport http zrev https://go.zrev.ai/mcp

{
  "mcpServers": {
    "zrev": {
      "url": "https://go.zrev.ai/mcp"
    }
  }
}

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

哪些应用可以使用它

依据这个服务器的连接方式和各应用自己的文档，检查于 2026年11月10日。

通过互联网连接，适用于

ClaudeChatGPTGooseLM StudioMsty StudioCherry StudioCursorVS Code + GitHub CopilotVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantClaude CodeCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatOpen WebUIRaycast

全部应用及其优缺点 →

它能做什么

工具名称和描述与服务器在 2026年11月9日 报告的完全一致。这些内容由作者撰写，不是我们写的；我们以纯文本形式显示。

• 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. Re

• get_benchmarks

  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 cave

• get_engagement_timeline

  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

• estimate_roi

  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

• grade_llms_txt

  Fetches https:// /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 f

• cold_read

  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 poi

• gtm_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 num

• book_call

  Returns the link and instructions for booking a free 30-minute intro call with Shivil Sahni, 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.

• leave_contact

  Records an email address, with optional name, company and note, so that Shivil Sahni, 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 g

基础检查i

我们检查的是任何人都能看到的信息；我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月11日

• ! 注意

  作者与仓库一致

  注册中心没有列出源代码仓库，因此无法将发布者（zrev.ai）与代码对应起来

• … 尚未检查

  仍在维护

  我们还没有从 GitHub 读取这个仓库

• … 尚未检查

  有许可证

  我们还没有从 GitHub 读取这个仓库

• ✓ 通过

  端点有响应

  在 88 毫秒内响应（HTTP 200）

• – 不适用

  使用 OAuth 登录

  无需登录

• ✓ 通过

  工具描述中没有隐藏指令

  已读取 9 个工具描述，未发现针对 AI 的隐藏指令

用户评价

还没有评价

用 Google 登录后写评价

与“搜索与 AI 记忆”中的其他服务器比较i

• 星标数多于此分类中其他 5940 个服务器的 0%
• 发布到注册中心的时间比此分类中其他 5940 个服务器的 41% 更新
• 基础检查结果等于或优于此分类中其他 5940 个服务器的 46%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时，星标数才计入。

替代选择

同一分类中其他作者的服务器，按星标数排序。

如何看卡片：↓ 每周下载量i★ GitHub 星标i三大支柱：评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

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Agent teams, swarms, memory and hooks for Claude Code and Codex, served over MCP.

作者：ruvnet·广泛使用·↓ 7.4万/周·★ 8.1万·今天更新

在你的电脑上运行

安装

Context7

Up-to-date docs for AI agents

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安装

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作者：metabase·广泛使用·★ 5万·6个月前更新

在云端运行

安装

作者的 README

仓库中没有找到 README