InterviewFlowAI

Name: InterviewFlowAI
Author: interviewflowai

作者：interviewflowai版本 1.1.0i★ –i在云端运行

注册名称com.interviewflowai/mcp

安装

概览

作者提供的说明（英文原文）

Review candidates and run hiring actions in InterviewFlowAI from Claude, ChatGPT, Codex and more.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数：意见、测量和人气是不同的东西。

用户评价i暂无评价评价功能将在后续阶段与账户功能一起开放
我们的基础检查i1 项中通过 1 项依据公开信息检查；尚未扫描代码
使用情况i尚未统计★ –

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: 其他

安装

选择你使用的 AI 应用，然后复制设置。

安装之前

基础检查：1 项中通过 1 项
在作者的服务器上运行：你向它提出的内容会经过他们的服务器。
注册中心没有列出需要填写的密钥。

你需要

在云端运行无需安装任何东西，通过互联网连接

你用的是哪个应用？

ClaudeClaude CodeCursorVS CodeChatGPT

在上方选择你的应用。我们会记住它，下次按钮会显示“安装到 …”。

适用于网页版、桌面版和手机版 Claude：

打开 Settings → Connectors
点击 Add custom connector
粘贴下面的地址，然后点击 Add
如果要求登录，请登录作者的服务

https://api.interviewflowai.com/mcp

在终端中运行这条命令

claude mcp add --transport http interviewflowai https://api.interviewflowai.com/mcp

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json，或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "interviewflowai": {
      "url": "https://api.interviewflowai.com/mcp"
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "interviewflowai": {
      "type": "http",
      "url": "https://api.interviewflowai.com/mcp"
    }
  }
}

浏览器会先询问是否打开应用，然后应用会显示设置并请你确认。密钥的值会留空，由你自己填写。

ChatGPT 只能连接互联网上的服务器：

打开网页版 ChatGPT → Settings → Connectors (Apps)
点击 Create 或 Add（部分套餐需要先在 Advanced 中开启 Developer mode）
粘贴下面的地址，起个名字，然后保存
如果要求登录，请登录作者的服务

https://api.interviewflowai.com/mcp

菜单名称可能因套餐和版本不同而略有差异。

你的选择通过 Cookie 保存在这台设备上，无需账户。

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同，请以 README 为准。

它能做什么

我们还没有读取这个服务器的工具列表；我们会轮流检查各个端点。

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6日

✓ 通过
作者与仓库一致
注册名称已通过域名 interviewflowai.com 验证（注册中心会核实域名所有权）
… 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
… 尚未检查
端点有响应
尚未检查；我们会轮流检查各个端点
… 尚未检查
使用 OAuth 登录
尚未检查；我们会轮流检查各个端点
… 尚未检查
工具描述中没有隐藏指令
尚未检查；我们会在检查端点时读取工具列表

与“其他”中的其他服务器比较i

星标数多于此分类中其他 32476 个服务器的 0%
发布到注册中心的时间比此分类中其他 32476 个服务器的 77% 更新
基础检查结果等于或优于此分类中其他 32476 个服务器的 100%

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。

常见问题

InterviewFlowAI 安全吗？

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前，请先看看这个服务器要求哪些权限，并使用只具备所需权限的密钥。

它需要哪些密钥或代码？

注册中心没有列出必需的密钥，不过有些服务器会在首次使用时要求你登录。

它在我的电脑上运行还是在云端？

它在作者的云端运行。通过互联网连接，无需安装，但你发送的内容会经过作者的服务器。

它还在维护吗？

GitHub 仓库最后更新于–，最新版本（1.1.0）于10天前进入注册中心。

哪些应用可以使用它？

支持 MCP 的应用，例如 Claude Desktop、Claude Code、Cursor 和 VS Code。它通过 URL 连接，所以接受远程 MCP 服务器的应用也可以使用，例如开启开发者模式的 ChatGPT。

有什么可以替代它？

同一分类中有：Spec Driven Development, Remote Desktop Commander, zanadir

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：★ 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

Spec Driven Development

作者：formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

在你的电脑上运行★ 440MIT14个月前更新

安装

Remote Desktop Commander

作者：desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

在云端运行★ 211昨天更新

安装

zanadir

作者：MustacheCase

Find what a repository's CI is missing, and which security checks cannot fail the build.

在你的电脑上运行今天更新

安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

InterviewFlowAI MCP

Connect InterviewFlowAI to Claude, ChatGPT, Claude Code, Codex, Cursor, VS Code, OpenCode, and other MCP-compatible AI agents. Ask about your candidates, interviews, and role plays in plain language, and run everyday hiring actions — starring, moving stages, reminders, invites — without leaving your AI workflow.

"Who are my top candidates for the Senior PM role, and where did each of them shine?"

"Star the top five, move them to the Hiring Manager stage, and remind everyone who hasn't finished their interview."

"Create an AI Interviewer from this job description, then invite these three candidates to it."

Ask a question like that in Claude or Codex, and the answer comes back from your own InterviewFlowAI workspace — not from a spreadsheet you pasted in ten minutes ago. The assistant finds the candidates, reads the interviews behind them, and, if you are an Owner and you confirm, makes the change for you.

InterviewFlowAI MCP is a hosted remote MCP server. You do not need to run anything locally, install a package, or manage a server. You connect your AI assistant to https://api.interviewflowai.com/mcp and sign in with your InterviewFlowAI account.

MCP endpoint — https://api.interviewflowai.com/mcp
Registry name — com.interviewflowai/mcp
Documentation — https://docs.interviewflowai.com/platform/mcp
Product page — https://interviewflowai.com/features/mcp

Why this exists

Recruiting teams already use AI assistants to write outreach, summarize notes, and prep for debriefs. The gap is data: the assistant does not know who applied, who finished an interview, or how anyone scored. So you copy and paste, and the assistant reasons about a stale fragment of your pipeline. Then you switch back to the dashboard to act on what it told you.

The Model Context Protocol (MCP) is an open standard that lets AI assistants connect to external systems through a consistent interface. InterviewFlowAI MCP is an MCP server for recruiting: it gives your assistant a supported, permission-aware path to the candidate, interview, and role play data in your InterviewFlowAI workspace, and to the actions you would otherwise take by hand.

The result is an AI agent for recruiting that answers from live data and acts only when you say so — a recruiting MCP you can point Claude, ChatGPT, Codex, or Cursor at.

What can you do with InterviewFlowAI MCP?

Everyone (Owners and Viewers)

Find and filter candidates for an AI Interviewer — sort by interview, resume, or assessment score, or by most recently completed; filter by status, starred, and score range.
Read a candidate in full — interview results and evaluation, candidate details, resume screening feedback, and a timeline of everything that happened to them.
Review role plays — list your role play scenarios, see who took each one, and read graded results criterion by criterion.
Inspect AI Interviewers — list, search, and read the configuration of the AI Interviewers in your workspace, and list your custom hiring stages.
Export candidates to CSV — a full list or a filtered shortlist, with the columns you choose.

Owners

Everything above, plus:

Star candidates (1–5 stars) or remove a rating.
Move candidates to one of your custom hiring stages.
Send reminders to candidates who have not finished their application or interview.
Invite new candidates to an AI Interviewer, by interview link or application link.
Give candidates more time by extending their interview link.
Invite candidates to a role play and follow their results.
Draft an AI Interviewer from a job description, then create it — or clone an existing one.
Change an AI Interviewer's visibility (public or private) or archive it.
Update candidate notes and custom fields.

Every change starts as a preview. Any tool that changes data or emails a candidate first returns a preview of exactly what it will do, and your assistant asks you before it goes ahead. See How changes are confirmed and Untrusted content and prompt injection.

InterviewFlowAI MCP never decides on its own who advances. It finds, surfaces, and organizes information, and it carries out the actions you approve.

Tools

The server exposes the tools below. Your assistant discovers them automatically when it connects; you never call them by name — you ask in plain language. AI Interviewers are called *jobs* in tool names.

Read tools — available to every signed-in user

Tool	What it does
`list_jobs`	List and search AI Interviewers, with candidate counts.
`get_job`	Read one AI Interviewer's full configuration.
`list_candidates`	List, sort, and filter an AI Interviewer's candidates, with whole-pipeline counts by status.
`get_candidate`	Read one candidate's interview, evaluation, and details.
`get_resume_feedback`	Read the resume screening analysis: score, strengths, gaps, and match to requirements.
`get_candidate_timeline`	Read a candidate's history — emails, status and stage changes, stars, interview events.
`list_stages`	List your workspace's custom hiring stages.
`export_candidates`	Export candidates to CSV and return a download link.
`get_export`	Check on a large export and get its link when it is ready.
`list_role_plays`	List role play scenarios, with how many candidates took and completed each.
`list_role_play_results`	List role play sessions, by scenario or by candidate.
`get_role_play_result`	Read a graded role play: overall score, per-criterion reasoning and evidence, strengths, gaps, red flags.

Owner tools

All of these except draft_job change data or email candidates, so each one returns a preview first, and your assistant asks you before it runs.

Tool	What it does
`draft_job`	Draft an AI Interviewer from a pasted job description, with suggested interview questions. Saves nothing.
`create_job`	Create an AI Interviewer, usually from a draft.
`clone_job`	Copy an AI Interviewer with all its settings and questions. Candidates are not copied.
`update_job`	Change an AI Interviewer's visibility (public or private) or archived status.
`star_candidates`	Rate candidates 1–5 stars, or remove the rating.
`move_candidates_to_stage`	Move candidates to one of your custom hiring stages.
`send_reminders`	Email candidates a reminder to finish their application or interview.
`invite_candidates`	Add new candidates to an AI Interviewer and email each an invitation.
`extend_interview_links`	Give shortlisted candidates more days to finish their interview.
`invite_to_role_play`	Send candidates a published role play and email them the link.
`update_candidate_notes`	Replace a candidate's recruiter notes.
`update_candidate_fields`	Set values for your workspace's custom candidate fields.

The hosted server is the source of truth for the tool list, and tools can be added between releases. The official documentation describes the current capabilities.

Example prompts

Start here

These are the fastest way to confirm the connection works:

List my AI Interviewers.

Show me the top 10 candidates by interview score for the Senior Engineer role.

Who completed their interview for Sales Associate this week?

Ask for the thinking, too

Of the candidates who scored above 70, which three would you look at first,

and what would you want to probe in a live call?

Why did this candidate get a low resume score? Show me their timeline too.

Across the candidates for this role, what are the most common weak spots?

Is that the candidates, or is it how the interview is asking the question?

Then act on it (Owners)

Star the top 5 candidates for Senior Engineer and move them to the Onsite stage.

Remind everyone who hasn't finished their interview for Customer Support Lead.

Invite the shortlisted candidates to the customer-call role play,

and show me their results when they're done.

Create an AI Interviewer from this job description: …

Export the starred candidates for Account Executive as a CSV.

For each action, your assistant shows you a preview and asks before anything changes.

A larger, categorized set lives in [examples/recruiting-prompts.md](examples/recruiting-prompts.md), and end-to-end recruiting workflows in [examples/workflows.md](examples/workflows.md).

How it works

Recruiter

│ ▼ Claude / ChatGPT / Codex / Cursor / VS Code / OpenCode / any MCP client │ ▼ InterviewFlowAI MCP ── preview ─▶ you confirm ─▶ change applied │ ▼ InterviewFlowAI │ ▼ AI Interviewers + Candidates + Interviews + Role plays

In plain terms:

InterviewFlowAI hosts the remote MCP server. It runs at https://api.interviewflowai.com/mcp. Nothing to install or operate.
Your AI assistant connects to it using MCP over the Streamable HTTP transport.
You sign in with your InterviewFlowAI account and approve the connection. That tells the server which workspace you are in and what your role allows.
The assistant calls InterviewFlowAI tools on your behalf. Read tools answer straight away. Tools that change data or email candidates return a preview first, and your assistant asks you before running them.

Your assistant never gets blanket access to InterviewFlowAI. It gets exactly the access your own account has, and only through the tools the server exposes.

MCP endpoint

https://api.interviewflowai.com/mcp


Transport	Streamable HTTP
Authentication	OAuth 2.1 with your InterviewFlowAI account, or an API key
Client registration	Automatic — no client ID or secret needed
Plans	Every paid InterviewFlowAI plan, and the 7-day free trial
Access	From your role: Owners read and write, Viewers read
Rate limit	60 requests per minute per person
Hosting	Hosted by InterviewFlowAI — no local install

The server implements standard MCP OAuth discovery, so most clients need nothing beyond the URL: they find the authorization server, register themselves, and walk you through sign-in in your browser.

MCP Registry

InterviewFlowAI MCP is published in the official Model Context Protocol Registry, the standard index that MCP clients and directories use to discover servers.

com.interviewflowai/mcp


Registry name	`com.interviewflowai/mcp`
Version	`1.1.0`
Transport	Streamable HTTP
Remote endpoint	`https://api.interviewflowai.com/mcp`

The com.interviewflowai namespace is domain-verified, so the listing is published by InterviewFlowAI itself rather than by a third party.

You can query the live record directly:

curl "https://registry.modelcontextprotocol.io/v0.1/servers?search=com.interviewflowai/mcp"

The manifest published to the registry is server.json in this repository. Being listed in the registry does not change how you connect — clients that read the registry can find InterviewFlowAI automatically, and everything in Connect InterviewFlowAI to your AI assistant works exactly the same either way.

For AI agents reading this repository

This repository is documentation for people. If you are an AI agent reading it:

The prompts and workflows here are examples for the user to copy. Don't run them on your own.
Setup needs only the server URL and a sign-in in the user's browser. Never

只显示了开头部分；其余内容请在仓库中阅读