What are the requirements to run this server?
You need an OpenReplay account with API access, a valid API key and project ID, network access to OpenReplay API endpoints, and Python 3.8+.
@rsp2k
基本情報
A Model Context Protocol (MCP) server for analyzing OpenReplay session recordings and user behavior patterns
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"openreplay-mcp-server": {
"command": "python",
"args": [
"-m",
"venv",
"venv"
]
}
}
}ツール一覧はまだありません
この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。
ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。
An MCP server that enables AI assistants to analyze OpenReplay session recordings and user behavior patterns. It searches, filters, and provides actionable insights from OpenReplay data, helping developers and UX researchers detect problems and understand user journeys.
Install by cloning the repository, creating a Python virtual environment, and installing dependencies. Configure OpenReplay API credentials in a .env file (API key, project ID, API URL). Run the server with python run_server.py or via Docker. Connect to an MCP client like Claude Desktop by adding the server to claude_desktop_config.json.
You need an OpenReplay account with API access, a valid API key and project ID, network access to OpenReplay API endpoints, and Python 3.8+.
Add the server to your `claude_desktop_config.json` under `mcpServers` with the `python` command pointing to `run_server.py` and environment variables for `OPENREPLAY_API_KEY` and `OPENREPLAY_PROJECT_ID`.
Tools include `search_sessions`, `get_session_details`, `get_user_session_history`, `analyze_user_journey`, `detect_problem_patterns`, `generate_session_summary`, and `find_similar_sessions`.
Yes. Use `docker-compose up` with environment variables in `.env`, or run `docker build` and `docker run` with the appropriate environment variables.
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Model Context Protocol Servers
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