ragtag-mcp: a local RAG setup for AI tools
Hacky MVP for local RAG search.
How to install
Clone repo
Clone this repo
cd /Users/suhdude/repos
gh repo clone weston-barger/ragtag-mcpPull down the codebases and documentation you want to index
You'll need local copies of the content you want to search. Example:
cd /Users/suhdude/repos
gh repo clone holoviz/param
gh repo clone holoviz/panel
gh repo clone bokeh/bokehSet up your rag_config.json file
Set up your rag_config.json to look like this
{
"indices": [],
"dbStoragePath": "/Users/suhdude/repos/ragtag-mcp/db",
"model": {
"embedding": "nomic-embed-text:latest",
"llm": "qwen3:latest"
}
}the dbStoragePath is where your vector database for searching will live.
Set up vitrual environment and install requirements
You'll want to set up a python venv for this repo. Run `` python3 -m venv .venv source ./.venv/bin/activate pip3 install -r requirements.txt --upgrade ``
Install Ollama + accompanying models
The main.py has a command that will help you do this on OSX. Run
python main.py osx_installNOTE: you'll want to re-run the osx_install command if you change the embedding or llm models in the config.
Create your RAG search indices
Now fill our the indices section of your rag_config.json file. There is an example in example_rag_config.json. Then run
python main.py buildThis may take a a bit. See python main.py build --help to see how to build one index at a time.
Claude instructions
Install the MCP server in your Claude project
Add
{
"mcpServers": {
"RAG": {
"command": "/Users/suhdude/repos/ragtag-mcp/.venv/bin/python3",
"args": [
"/Users/suhdude/repos/ragtag-mcp/main.py",
"serve"
]
}
}
}to your .mcp.json file for your project.
Check install
Open claude an issue /mcp. You should see RAG!
Optional: Tell Claude to use the RAG search to get context
Add something to your CLAUDE.md file to tell it to use RAG to get context
(My project) relies on packages pkg1, pkg2, pkg3. Use the RAG search MCP server to understand how to use packages pkg1, pkg2, pkg3 when planning code changes. 
