Agent Interviews MCP Server for Scalable Qualitative Research

Overview

Agent Interviews MCP Server implements a lightweight Model Context Protocol (MCP) host tailored for running scalable, AI-assisted qualitative research workflows. It provides a central API and orchestration layer where interview “agents” can recruit participants, conduct interviews or surveys, store transcripts, and run automated coding and insight extraction. The server abstracts common operational concerns—state, storage, tool integration—so research teams can focus on designing prompts and analysis logic.

The MCP server is useful when you need to run many interviews or surveys in parallel, standardize how agent-based interviews are executed, and connect LLM-powered analysis to a persistent data layer (search, vector DB, analytics). Typical deployments integrate the MCP server with an LLM provider, a vector database for retrieval-augmented tasks, and messaging or web channels for participant interaction.

Features

• Host for MCP-compliant agents: run interview agents that request context, persist state, and call tools.
• Interview orchestration: schedule, queue, and run interviews or surveys at scale.
• Transcript management: persist raw text, metadata, timestamps, and versioned agent outputs.
• Automated coding and insights: run model-powered tagging, summarization, and thematic extraction.
• Search and retrieval: integrate with vector or text search engines to find previous answers and contexts.
• Extensible tool stack: plugin connectors for LLMs, messaging channels, databases, and analytics.
• REST API + WebSocket hooks for real-time agent communication and monitoring.
• Config-driven deployment (Docker, env files) for cloud or local testing.

Installation / Configuration

Below are typical steps to get a development instance running. The project supports Docker for quick setup or a Python runtime for local development.

Clone the repository and start with Docker Compose (recommended):

git clone https://github.com/thinkchainai/agentinterviews_mcp.git
cd agentinterviews_mcp
docker compose up --build

Environment variables (example .env):

# Server
MCP_HOST=0.0.0.0
MCP_PORT=8080

# Storage
DATABASE_URL=postgresql://user:pass@db:5432/agent_interviews

# Vector DB / Search (optional)
VECTOR_DB_URL=http://vectordb:8000
VECTOR_DB_API_KEY=your-vector-key

# LLM provider
LLM_PROVIDER=openai
OPENAI_API_KEY=sk-...

# Security
MCP_API_KEY=change-me

Run locally with Python (if provided):

# create venv and install
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

# set env vars
export $(cat .env | xargs)

# run server
uvicorn mcp_server.app:app --host 0.0.0.0 --port 8080 --reload

Database migration example (if Alembic or similar is used):

alembic upgrade head

Adjust configuration to point the vector DB and LLM provider to your accounts. For production, secure MCP_API_KEY, use HTTPS, and configure persistent volumes for storage.

Available Resources

• REST API endpoints (examples)

  • GET /health — basic liveliness/readiness
  • POST /interviews — create a new interview or survey job
  • GET /interviews/{id} — fetch interview state and transcripts
  • POST /interviews/{id}/responses — append participant responses
  • POST /agents/{agent_id}/invoke — invoke an agent-run step or tool
  • GET /search — search transcripts and coded outputs

• WebSocket hooks for real-time agent events (agent status, live transcript streaming).

• CLI helper scripts to bootstrap interview templates and seed recruitment lists.

• Sample agents and prompt templates in the repo (look for examples/ or templates/).

• Integration adapters: LLM provider connector, vector DB connector, SMTP/webhook recruitment adapters.

Table: Key environment variables

Use Cases

• Large-scale qualitative interviewing: launch hundreds of semi-structured interviews in parallel, using agent logic to adapt follow-ups and ensure consistent probing across participants.
• Mixed-method surveys: combine structured survey questions with open-text follow-ups that agents summarize and code automatically for rapid thematic analysis.
• Recruitment automation: use agents to screen participants, schedule interviews, and trigger reminders via email or messaging integrations.
• Analyst augmentation: automatically run initial coding and summarization, then export structured datasets for human analysts to refine and triangulate insights.
• Longitudinal research: track participant responses over time, use retrieval-augmented prompts to reference prior interviews, and detect changing themes.

Getting Started Tips

• Start with a small pilot: create a simple interview template and run a handful of sessions to validate that transcription, storage, and automated coding work as expected.
• Secure production endpoints: enforce API keys, rate limits, and TLS. Store keys and PII in secure stores.
• Instrument for metrics: collect timing, agent execution counts, and model token usage to estimate cost and performance.
• Reuse prompts as templates: version prompts and agent flows so you can iterate without affecting live interviews.

For full API reference, example agents, and deployment patterns, consult the repository at https://github.com/thinkchainai/agentinterviews_mcp and the included examples directory.