InterviewFlowAI

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

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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.

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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.

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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

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.

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.

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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).

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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:

1. InterviewFlowAI hosts the remote MCP server. It runs at https://api.interviewflowai.com/mcp. Nothing to install or operate.
2. Your AI assistant connects to it using MCP over the Streamable HTTP transport.
3. 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.
4. 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.

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MCP endpoint

https://api.interviewflowai.com/mcp

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.

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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

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.

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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