InterviewLM's hiring surface is now a remote MCP server. Connect Claude, Cursor, Codex, or Copilot and post roles, screen candidates, monitor live interviews, and make hiring decisions — in the tools you already work in.
3 free credits · no card · connect in ~2 minutes
Works with Claude Code · Codex · GitHub Copilot · Cursor · Antigravity · Claude Desktop · ChatGPT
You › Draft a Senior Backend Engineer pipeline and invite my shortlist.
Agent › analyze_jd ✓ propose_pipeline_from_jd ✓ create_pipeline ✓
Agent › add_candidates ✓ invite_candidate ×3 ✓ (emails sent)
Agent › Done. 3 candidates invited to "Backend Engineer — Senior".
Your ATS, your assessment tool, email, calendar, docs, a spreadsheet — every hire is a context-switching tax. Meanwhile you already run half your day through an AI agent. So run hiring there too.
The InterviewLM MCP server exposes the hiring-manager surface — pipelines, candidates, job-description tooling, live monitoring, evaluations, and hiring decisions — as a remote Model Context Protocol server. Any MCP-capable agent — Claude Code, Cursor, Codex, Antigravity, GitHub Copilot, Claude Desktop, or ChatGPT — connects over OAuth 2.1 and runs the full hiring lifecycle on your behalf through 52 permissioned tools — every request scoped to your organization.
No API keys or manual token setup. Add the InterviewLM connector URL (or one npx command) to your agent, then approve a browser consent screen — OAuth 2.1 with PKCE. You choose the organization and the scopes.
Talk to it in plain language: “Draft a pipeline for a Senior Backend Engineer,” “who’s waiting on review?”, “invite the top three.” No new dashboard to learn.
The agent calls real tools on your behalf — creating pipelines, sending real invitation emails, reading evaluations, and (only if you grant it) recording hiring decisions.
52 tools across the whole hiring lifecycle.
Explore role families, resolved stage sequences, and scoring dimensions before you build.
Turn a job description into a recommended, editable hiring pipeline — then create it.
Add candidates, send (or re-send) stage invitations, and advance them through stages.
Track who's in progress, who needs attention, and how a pipeline's funnel is moving.
Pull full evaluations, per-candidate reports, and signed session-replay manifests.
Record hire / reject / manual-round decisions — gated behind a separate, opt-in scope.
See dashboards and leaderboards, and check your team, plan, and credit balance.
Granular OAuth scopes — read-only by default; you pick what to grant.
Server-rendered consent screen, bound to a single organization.
Hire / reject decisions sit behind a separate, opt-in scope.
Every query is scoped to your org; demo data is always excluded.
Server-side tokens stored hashed; local helper tokens stay on your machine (0600) and can be revoked.
Every action recorded in the event-sourced audit trail.
Copy one command into the agent you already use. Native connectors run the OAuth consent in your browser; stdio-only clients use the @interviewlm/mcp helper, which handles OAuth for you.
Native remote connector — Claude Code runs the OAuth consent for you.
Run in your terminal
claude mcp add --transport http interviewlm https://interviewlm.com/api/mcpstdio via the @interviewlm/mcp helper — OAuth opens in your browser on first run.
Run in your terminal
codex mcp add interviewlm -- npx -y @interviewlm/mcpNative remote connector in VS Code — Copilot handles OAuth.
Run in your terminal (VS Code CLI)
code --add-mcp '{"name":"interviewlm","type":"http","url":"https://interviewlm.com/api/mcp"}'Remote server — Cursor runs the OAuth consent in-browser.
Add to .cursor/mcp.json
{
"mcpServers": {
"interviewlm": {
"url": "https://interviewlm.com/api/mcp"
}
}
}stdio via the @interviewlm/mcp helper.
Settings → Customizations → Open MCP Config (mcp_config.json)
{
"mcpServers": {
"interviewlm": {
"command": "npx",
"args": ["-y", "@interviewlm/mcp"]
}
}
}Add as a custom connector — works in Claude Desktop and claude.ai.
Settings → Connectors → Add custom connector
https://interviewlm.com/api/mcpAdd as a custom connector (Plus, Pro, Business & Enterprise).
Settings → Connectors → Add custom connector (Developer mode)
https://interviewlm.com/api/mcpUsing another MCP client? Point it at https://interviewlm.com/api/mcp, or run npx -y @interviewlm/mcp.
On top of the raw tools, a free library of 13 role playbooks and 7 hiring workflows encodes the right stage sequence and rubric for each role family — with confirm-before-mutate, so decisions always require your evidence review and confirmation.
MCP is an open standard that lets AI agents securely connect to external tools and data. InterviewLM exposes its hiring surface as a remote MCP server, so any MCP-capable agent — Claude Code, Cursor, Codex, Antigravity, GitHub Copilot, Claude Desktop, or ChatGPT — can run your hiring workflow through a set of well-defined, permissioned tools.
Any MCP-capable client. We have one-line setup for Claude Code, Codex, GitHub Copilot, Cursor, Antigravity, Claude Desktop, and ChatGPT. Native-remote clients connect to the hosted endpoint directly and run OAuth in-browser; stdio-only clients use the npx @interviewlm/mcp helper, which handles OAuth for them.
You connect through OAuth 2.1 with PKCE and approve a consent screen that lists exactly which scopes the agent gets, bound to one organization. Hire/reject decisions sit behind a separate opt-in scope. Every request is scoped to your org, server-side tokens are stored hashed, local helper tokens stay on your machine and can be revoked, and every action is recorded in the audit log.
No. There are no API keys or manual tokens to manage. You add the connector URL (or one npx command) to your agent and approve the consent screen — then you work in plain language.
The MCP server is included with InterviewLM. You only pay for assessments you run — from $4.00 per AI interview, with 3 free credits to start and no credit card required. Reading pipelines, candidates, and reports through the MCP server doesn't cost credits; sending a candidate to a stage does, exactly as it would in the app.
Only if you grant the separate decision scope at consent time. With it, the agent can record hire, reject, and manual-round decisions — the same actions you'd take in the dashboard, written through the same services, and always attributed to you in the audit trail.