InterviewFlowAI MCP server

InterviewFlowAI MCP

Connect InterviewFlowAI to Claude, ChatGPT, Codex, and other MCP-compatible AI assistants. Ask about your candidates and interviews in plain language, and perform supported recruiting actions without leaving your AI workflow.

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

"Compare our top two finalists on communication and problem-solving,
 and draft a summary for the hiring manager."

"Show me everyone who completed the interview this week,
 grouped into strong, maybe, and no."

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 answers the question you actually had.

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.

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 and interview data already in your InterviewFlowAI workspace.

The result is an AI agent for recruiting that answers from live data — a recruiting MCP you can point Claude, ChatGPT, or Codex at.

What can you do with InterviewFlowAI MCP?

With read access (mcp:read), a connected assistant can:

• Find candidates in your workspace, including by interview completion status.
• Inspect candidate information — the supported fields on a Candidate record.
• Retrieve AI Interviewer information — list the AI Interviewers configured in your workspace and inspect their details.
• Review interview information, including interview scores.
• Summarize candidate and interview context so you can read the substance of an interview in seconds instead of scanning a full transcript.

With write access (mcp:write, workspace Owners only), a connected assistant can:

• Update supported candidate fields — notes, custom fields, visibility, and archived status.

That is the supported surface. InterviewFlowAI MCP does not screen, rank, reject, or advance anyone on its own — it finds, surfaces, and organizes information so a recruiter can decide.

A note on tool names. This repository documents capabilities rather than individual tool names, because the exact tool list is defined by the hosted server and can change between releases. Your assistant discovers the current tools automatically when it connects. See the official documentation for the authoritative capability list.

Example prompts

Find completed interviews

Show me the candidates who completed the interview.

Find top candidates

Show me the top 10 candidates by interview score.

Filter by score

Show me candidates with an interview score of 70 or above.

Going further

Shortened. The full README is on GitHub.