RAGSync

What it is

A configuration-driven RAG MCP server. One YAML file defines the sources, chunking strategy, embedding model and vector store; the server indexes them, watches them for changes, and exposes a small, source-agnostic tool surface on top.

What you get

• Five tools that stay the same as sources come and go: search, list_sources, get_document, get_index_status, reindex
• search — semantic search across one source or all of them, with optional metadata filtering, returning normalized [0, 1] scores
• get_document — the full document once search surfaces a chunk from it
• get_index_status and reindex — freshness and health per source, and a forced re-scan when you need one
• Source types: folder for a local directory of text, PDF and Markdown files with gitignore-style include and exclude globs, and website for a fixed list of pages, fetched rather than crawled
• Live reload — filesystem watching and polling keep the index current, and editing the config itself applies without a restart
• Tools return structured {"error": "..."} objects rather than raising, so a calling agent can recover in conversation

Requirements

Run it with uvx ragsync --config /abs/path/to/config.yaml — no clone, no install step. Give --config an absolute path; paths inside the config resolve against the config file's own directory, so a repo config can use path: ./docs. Embeddings default to local fastembed, which needs no API key; openai and voyage are per-source alternatives and read their key from the environment variable named by api_key_env, never from the config. First launch downloads the local embedding model, so it is slower than later ones. Access scoping is by running separate instances with separate configs — there is no cross-instance search.

Setup effort

One command — uvx ragsync --config <YOUR_PATH>

Tools you get

5 tools named in the project's documentation.

Requires

uvx on PATH

Where it runs

All 4 documented clients run the server on your machine.