Manual setup required. The maintainer's config contains paths only you know - edit the placeholders below before adding it to Claude Code.
1
Prepare the server locally
Run this once before adding it to Claude Code.
git clone https://github.com/gogabrielordonez/mcp-ragchat
cd mcp-ragchat
npm install
npm run build2
Register it in Claude Code
claude mcp add mcp-ragchat -- node /absolute/path/to/mcp-ragchat/dist/mcp-server.jsReplace any placeholder paths in the command with the real path on your machine.
3
Make your agent remember this setup
mcp-ragchat's config, env vars, and the gotchas you hit — recalled in every future Claude Code, Cursor, and Codex session.
npx conare@latestFree · one command · indexes the sessions already on disk. Set up in the browser instead →
What it does
- Builds local vector stores from markdown content
- Generates embeddable chat widgets for websites
- Supports multiple LLM providers including OpenAI, Anthropic, and Google Gemini
- Runs entirely locally with no cloud infrastructure required
- Provides built-in testing for RAG retrieval quality
Tools 5
ragchat_setupSeed a knowledge base from markdown content where each ## section becomes a searchable document.ragchat_testSend a test message to verify RAG retrieval and LLM response quality.ragchat_serveStart a local HTTP chat server with CORS and input sanitization.ragchat_widgetGenerate a self-contained <script> tag for a floating chat bubble.ragchat_statusList all configured domains with document counts and config details.Environment Variables
OPENAI_API_KEYAPI key for OpenAI LLM providerANTHROPIC_API_KEYAPI key for Anthropic LLM providerGEMINI_API_KEYAPI key for Google Gemini LLM providerTry it
→Add AI chat to mysite.com. Here's the content: [paste your markdown]
→Test the RAG retrieval for mybakery.com by asking 'What are your hours?'
→Generate the embed code for the chat widget for mybakery.com
→Show me the status of all configured domains and their document counts
Original README from gogabrielordonez/mcp-ragchat
<h1 align="center">mcp-ragchat</h1>
An MCP server that adds RAG-powered AI chat to any website. One command from Claude Code.
Tell Claude Code "add AI chat to mysite.com" and it will crawl your content, build a local vector store, spin up a chat server, and hand you an embed snippet. No cloud infra. No database. Just one API key.
Demo
You: "Set up AI chat for mybakery.com using this FAQ and menu content"
Claude: I'll set up RAG-powered chat for mybakery.com.
[1/4] ragchat_setup
Splitting content into sections...
Generating embeddings for 12 documents...
Domain "mybakery.com" configured with 12 documents.
[2/4] ragchat_test
Query: "What are your hours?"
Reply: "We're open Monday-Saturday 7am-6pm, and Sunday 8am-2pm."
RAG Sources: mybakery.com-3 (0.91), mybakery.com-7 (0.84)
Latency: 340ms
[3/4] ragchat_serve
Chat server running at http://localhost:3456
POST /chat ready.
[4/4] ragchat_widget
Generated embed code. Paste this into your HTML:
<script src="http://localhost:3456/widget.js"></script>
You: Done. Live chat on my site in under 60 seconds.
Quick Start
1. Clone and build
git clone https://github.com/gogabrielordonez/mcp-ragchat
cd mcp-ragchat
npm install && npm run build
2. Configure Claude Code (~/.claude/mcp.json)
{
"mcpServers": {
"ragchat": {
"command": "node",
"args": ["/absolute/path/to/mcp-ragchat/dist/mcp-server.js"],
"env": {
"OPENAI_API_KEY": "sk-..."
}
}
}
}
3. Use it
Open Claude Code and say:
"Add AI chat to mysite.com. Here's the content: [paste your markdown]"
Claude handles the rest.
Tools
| Tool | What it does |
|---|---|
ragchat_setup |
Seed a knowledge base from markdown content. Each ## section becomes a searchable document with vector embeddings. |
ragchat_test |
Send a test message to verify RAG retrieval and LLM response quality. |
ragchat_serve |
Start a local HTTP chat server with CORS and input sanitization. |
ragchat_widget |
Generate a self-contained <script> tag -- a floating chat bubble, no dependencies. |
ragchat_status |
List all configured domains with document counts and config details. |
How It Works
+------------------+
| Your Markdown |
+--------+---------+
|
ragchat_setup
|
+------------v-------------+
| Local Vector Store |
| ~/.mcp-ragchat/domains/ |
| vectors.json |
| config.json |
+------------+-------------+
|
User Question |
| |
+------v------+ +------v------+
| Embedding | | Cosine |
| Provider +->+ Similarity |
+-------------+ +------+------+
|
Top 3 chunks
|
+----------v-----------+
| System Prompt |
| + RAG Context |
| + User Message |
+----------+-----------+
|
+----------v-----------+
| LLM Provider |
+----------+-----------+
|
Reply
Everything runs locally. No cloud infrastructure. Bring your own API key.
Supported Providers
LLM (chat completions)
| Provider | Env Var | Default Model |
|---|---|---|
| OpenAI | OPENAI_API_KEY |
gpt-4o-mini |
| Anthropic | ANTHROPIC_API_KEY |
claude-sonnet-4-5-20250929 |
| Google Gemini | GEMINI_API_KEY |
gemini-2.0-flash |
Embeddings (vector search)
| Provider | Env Var | Default Model | |---