RAGSync MCP Server

Index your docs, files, and websites into a vector store and give your AI agent live semantic search with auto-syncs on changes, zero code required.

Remote serverstreamable-httpPython

What is the RAGSync MCP Server?

Index your docs, files, and websites into a vector store and give your AI agent live semantic search with auto-syncs on changes, zero code required.

What it actually does

Configuration-driven RAG MCP server — ingest, watch, and search arbitrary knowledge sources behind a stable tool surface.

  • Broad source support — — index local folders (text, PDF, Markdown) and web pages; not limited to a single file type or format
  • Config-driven — — one YAML file defines sources, chunking strategy, embedding model, and vector store; no code required
  • Live reload — — filesystem watching and polling keep the index current as sources change; editing the config itself applies changes without a restart
  • Flexible embeddings — — local fastembed works out of the box with no API key; swap in OpenAI or Voyage per source
  • Stable MCP tool surface — — five source-agnostic tools (search, list_sources, get_document, get_index_status, reindex) that never change as sources are added

More MCP options

Compare MCP servers by category, or browse hosted MCP servers if you prefer an endpoint over a local process.

Maintainer setup documentation.

How to install the RAGSync MCP Server

{
  "mcpServers": {
    "ragsync": {
      "command": "uvx",
      "args": [
        "ragsync",
        "--config",
        "/abs/path/to/config.yaml"
      ]
    }
  }
}

From the project documentation. Replace placeholders and example paths before connecting.

Frequently asked questions

Index your docs, files, and websites into a vector store and give your AI agent live semantic search with auto-syncs on changes, zero code required. Confirm the tools offered by your installed version in the connected client. Tool availability and permissions depend on the server and account configuration.