The AI Interview Agents MCP Server enables you to manage an end-to-end AI-driven hiring pipeline via natural language from any MCP client, covering everything from role creation to scored interview reports.
Role Management
List existing job roles and create new ones from job descriptions
Set up dedicated screening roles with skills, experience, location, and work mode requirements
Candidate & CV Processing
Parse CV/resume files (PDF, DOCX, up to 20 per call) with OCR fallback to extract candidate info
Add parsed candidates to screening roles and reassign candidates between roles
CV Screening & Ranking
Trigger async AI scoring of CVs against a job description
Retrieve ranked results with scores (0–100), verdicts (strong/possible/weak/reject), matched/missing skills, and location fit
Interview Scheduling & Management
Schedule AI-driven voice interviews (screening, technical, or HR types; multilingual support)
Use dry-run mode to preview configuration before sending invites or consuming quota
Reschedule, cancel, and send reminder emails for interviews
Schedule top-ranked screened candidates directly for interviews
Question Preview
Generate and preview AI-tailored interview questions before committing to a schedule
Reporting & Pipeline
List interviews with filters (role, candidate, status, date)
Retrieve full interview details including transcripts and feedback
Access scored reports with analysis summaries, strengths, weaknesses, and recommendations
View the candidate pipeline funnel (invited/scheduled/completed/no-show) per role
Provides authentication integration with Firebase, enabling secure user sign-in and bearer token validation for API requests.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@AI Interview Agents MCP Server List all candidates for the senior developer role."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
The official Model Context Protocol server for AI Interview Agents, a voice-AI hiring platform. It lets any MCP client (Claude, Cursor, custom agents) drive the recruiting workflow in natural language: create roles, screen CVs, schedule and manage candidate interviews, and read back scored reports with transcripts.
The live voice interview itself is out of scope here (that runs on a separate real-time service). This server triggers interviews and reads back the results.
Roles — list your roles, or create one from a job description.
Candidates & CVs — parse dropped CV/PDF files, normalize messy pasted candidate lists (names, emails, phone numbers, experience, skills), and reassign candidates between roles.
Interviews — schedule interviews (with a dry_run mode that normalizes and previews without sending anything or consuming quota), reschedule, cancel, and send reminders.
Reports — read the candidate pipeline and pull back scored interview reports with transcripts.
Related MCP server: job-search-mcp
The server runs remotely with OAuth sign-in. Point any Streamable-HTTP MCP client at:
{
"mcpServers": {
"ai-interview-agents": {
"type": "streamable-http",
"url": "https://mcp.aiinterviewagents.com/mcp"
}
}
}Your client discovers the auth flow automatically via /.well-known/oauth-protected-resource and signs you in with your AI Interview Agents account. It is also listed in the official MCP Registry as com.aiinterviewagents/interviews.
pip install -e .
export AIIA_FIREBASE_TOKEN="<your token>" # or: aiia-mcp login
aiia-mcpAIIA_BACKEND_URL overrides the API base if you are pointing at your own deployment.
The server holds no account secrets. Each request carries the user's own Firebase ID token, forwarded to the backend as a bearer token — the same trust path as the web app. For the hosted transport, an OAuth layer (dynamic client registration + PKCE) bridges MCP clients to Firebase sign-in.
pip install -e ".[dev]"
pytest
ruff check .MIT — see LICENSE.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
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