transcript-search 0.1.0

MCP server for semantic search over meeting transcripts (multilingual: EN/UK/RU)

Transcript Search MCP Server

Semantic search over meeting transcripts using Qdrant + multilingual embeddings, exposed as a Claude Code MCP server.

Setup

# Start Qdrant
docker compose up -d

# Create Python environment
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

# Index all transcripts (~2-5 min first run, downloads model)
TRANSCRIPTS_DIR=/path/to/your/transcripts python index.py

# Verify: Qdrant dashboard at http://localhost:6333/dashboard

Configuration

Set TRANSCRIPTS_DIR to the folder containing your .md transcript files. Defaults to ./transcripts in the current working directory.

Usage

CLI

# Basic search
TRANSCRIPTS_DIR=/path/to/transcripts python search_cli.py "what did Dima say about department metrics"

# Filter by speaker
python search_cli.py "project updates" --speaker "Dima Batt" --top-k 3

# Filter by meeting type
python search_cli.py "purpose discussion" --type 1on1

# Multilingual query
python search_cli.py "культура як диференціатор"

MCP Server (Claude Code integration)

Add to your project's .mcp.json:

{
  "mcpServers": {
    "transcript-search": {
      "command": "uv",
      "args": [
        "run",
        "--directory", "/path/to/transcript-search",
        "--python", "/path/to/transcript-search/.venv/bin/python",
        "mcp_server.py"
      ],
      "env": {
        "TRANSCRIPTS_DIR": "/path/to/your/transcripts"
      }
    }
  }
}

Architecture

Transcripts (*.md) → parse_transcripts.py → chunk.py → index.py → Qdrant
                                                                      ↑
                                            search_cli.py / mcp_server.py

• Model: intfloat/multilingual-e5-large (1024-dim, supports UK/RU/EN)
• Chunking: Speaker-turn-aware, ~1500 tokens per chunk, 3-turn overlap
• Storage: Qdrant (Docker) with cosine similarity