Which integrations are available for this server?

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.

How do I use mcp-ragchat?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. 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.

mcp-ragchat by gogabrielordonez

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
OpenAI	`OPENAI_API_KEY`	`text-embedding-3-small`
Google Gemini	`GEMINI_API_KEY`	`text-embedding-004`
AWS Bedrock	`AWS_REGION` + IAM	`amazon.titan-embed-text-v2:0`

Override defaults with LLM_MODEL and EMBEDDING_MODEL environment variables.

Architecture

~/.mcp-ragchat/domains/
  mysite.com/
    config.json     -- system prompt, settings
    vectors.json    -- documents + embedding vectors

Vector 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.

Contributing

Issues and pull requests are welcome.

Found a bug? Open an issue
Want to add a feature? Fork, branch, PR.
Questions? Start a discussion

Star History

Enterprise

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

mcp-ragchat