Generate QA datasets & evaluate RAG systems. Privacy-first, any LLM, local or cloud.
Generate QA datasets & evaluate RAG systems. Privacy-first, any LLM, local or cloud. That is what the ragscore mcp server brings to an AI assistant: the same capability, reachable through the Model Context Protocol rather than a separate app or dashboard.
🔒 Privacy-First • ⚡ Lightning Fast • 🤖 Any LLM • 🏠 Local or Cloud • 🌍 Multilingual
Installation goes through your MCP client rather than a global install: point it at ragscore on PyPI and it is fetched when the client starts. The copy-paste blocks for Claude Desktop, Claude Code and Cursor are further down this page.
Configuration is passed through the environment: OPENAI_API_KEY, ANTHROPIC_API_KEY, DASHSCOPE_API_KEY, LLM_BASE_URL, RAGSCORE_CHUNK_SIZE, RAGSCORE_QUESTIONS_PER_CHUNK, RAGSCORE_WORK_DIR, RAGSCORE_NO_TELEMETRY. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.
Plenty of AI and media services servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. It is maintained by HZYAI; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
{
"mcpServers": {
"ragscore": {
"command": "uvx",
"args": ["ragscore"],
"env": {
"OPENAI_API_KEY": "your-value",
"ANTHROPIC_API_KEY": "your-value",
"DASHSCOPE_API_KEY": "your-value",
"LLM_BASE_URL": "your-value",
"RAGSCORE_CHUNK_SIZE": "your-value",
"RAGSCORE_QUESTIONS_PER_CHUNK": "your-value",
"RAGSCORE_WORK_DIR": "your-value",
"RAGSCORE_NO_TELEMETRY": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
| Variable | Description | Required |
|---|---|---|
| OPENAI_API_KEY | Credential the server authenticates with. | Yes |
| ANTHROPIC_API_KEY | Credential the server authenticates with. | Yes |
| DASHSCOPE_API_KEY | Credential the server authenticates with. | Yes |
| LLM_BASE_URL | Endpoint or connection string the server talks to. | Yes |
| RAGSCORE_CHUNK_SIZE | Configuration value read at startup. | Optional |
| RAGSCORE_QUESTIONS_PER_CHUNK | Configuration value read at startup. | Optional |
| RAGSCORE_WORK_DIR | Filesystem location the server is allowed to use. | Optional |
| RAGSCORE_NO_TELEMETRY | Configuration value read at startup. | Optional |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.