Utilizes AWS Bedrock to generate vector embeddings for building the server's local searchable knowledge base.
Integrates with Google Gemini models to provide both text embedding generation and AI chat completions.
Processes Markdown content to seed a local vector store, treating header-delimited sections as searchable documents.
Connects to OpenAI's API for generating text embeddings and providing chat completions to power the RAG pipeline.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@mcp-ragchat set up AI chat for mybakery.com using the menu and FAQ content"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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.
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.Related MCP server: Crawl4AI MCP Server
1. Clone and build
git clone https://github.com/gogabrielordonez/mcp-ragchat
cd mcp-ragchat
npm install && npm run build2. 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.
Tool | What it does |
| Seed a knowledge base from markdown content. Each |
| Send a test message to verify RAG retrieval and LLM response quality. |
| Start a local HTTP chat server with CORS and input sanitization. |
| Generate a self-contained |
| List all configured domains with document counts and config details. |
+------------------+
| 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 |
+----------+-----------+
|
ReplyEverything runs locally. No cloud infrastructure. Bring your own API key.
Provider | Env Var | Default Model |
OpenAI |
|
|
Anthropic |
|
|
Google Gemini |
|
|
Provider | Env Var | Default Model |
OpenAI |
|
|
Google Gemini |
|
|
AWS Bedrock |
|
|
Override defaults with LLM_MODEL and EMBEDDING_MODEL environment variables.
~/.mcp-ragchat/domains/
mysite.com/
config.json -- system prompt, settings
vectors.json -- documents + embedding vectorsVector store -- Local JSON files with cosine similarity search. Zero external dependencies.
Chat server -- Node.js HTTP server with CORS and input sanitization.
Widget -- Self-contained <script> tag. No frameworks, no build step.
Issues and pull requests are welcome.
Found a bug? Open an issue
Want to add a feature? Fork, branch, PR.
Questions? Start a discussion
Need multi-tenancy, security guardrails, audit trails, and managed infrastructure? Check out Supersonic -- the enterprise AI platform built on the same RAG pipeline.
MIT License -- Gabriel Ordonez
Start a local HTTP chat server for a domain. The server runs on localhost and handles POST /chat requests. Use ragchat_widget to get the embed code that connects to this server.
Inputs: domain (required), port.
Initialize a domain with a knowledge base from markdown content. Each ## section becomes a searchable document with vector embeddings. This is the first step — run this before testing or serving.
Inputs: domain (required), content (required), systemPrompt (required).
List all configured domains with document counts and config status. Shows what's been set up and what's ready to serve.
Inputs: no named parameters.
Send a test message to a domain's chat. Uses RAG search + LLM to generate a response, same as production. Good for verifying the knowledge base works.
Inputs: domain (required), message (required).
Generate an embeddable chat widget. Returns a tag that creates a floating chat bubble on any webpage. Connects to the chat server started with ragchat_serve.
Inputs: domain (required), chatUrl, title, color.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
Scored across 5 tools
Each tool targets a distinct operation: setup initializes a domain, status checks configuration, test sends a query, serve starts a server, and widget generates embed code. There is no overlap in functionality.
All tools share the consistent prefix 'ragchat_', but the second part mixes verbs (serve, setup, test) and nouns (status, widget). This is mostly predictable, though not strictly verb_noun.
With 5 tools, the server covers the essential lifecycle of a RAG chat system (setup, verify, test, deploy, embed) without unnecessary bloat or missing critical steps.
The tools cover the core workflow: initialize, check status, test, serve, and embed. Minor gaps exist (e.g., no update or delete domain), but the surface is complete for basic usage.
One MCP server for all web data. Scrape, search, crawl, and AI research in one install.
The CustomGPT.ai MCP server is a fully managed, RAG-powered endpoint that connects large language models with private knowledge bases and external data sources. It provides tools for retrieval-augmented generation queries (send_message), data ingestion (upload_file), and source listing, enabling AI agents to query private documents like PDFs with high accuracy and real-time citations.
MCP server that lets your AI assistant build and deploy apps, hosting each one at a live URL.
Self-hosted AI workspace: semantic RAG document search, long-term memory, and AI chat via MCP.
