# gogabrielordonez/mcp-ragchat [Health: Active]

**Category:** 🔎 end to end RAG platforms  
**Repository:** https://github.com/gogabrielordonez/mcp-ragchat  
**GitHub Stars:** 2  
**Views:** 2  
**Installs:** 0  
**Upvotes:** 0  
**Directory Page:** https://allmcps.com/mcp/gogabrielordonez-mcp-ragchat

## Description
Add RAG-powered AI chat to any website with one command. Local vector store, multi-provider LLM (OpenAI/Anthropic/Gemini), self-contained chat server and embeddable widget.

## Tools
Capabilities this server exposes over MCP:

- **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.

## Claude Desktop Quick Installation
Heuristic fallback — verify the package name and runner against the repository README before running it. Uses `npx` (confidence: low):

```json
"mcpServers": {
  "mcp-ragchat": {
    "command": "npx",
    "args": ["-y","gogabrielordonez-mcp-ragchat"],
    "env": {
      "OPENAI_API_KEY": "",
      "ANTHROPIC_API_KEY": "",
      "GEMINI_API_KEY": "",
      "AWS_REGION": "",
      "LLM_MODEL": "",
      "EMBEDDING_MODEL": ""
    }
  }
}
```

**Requires environment variables:** `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, `GEMINI_API_KEY`, `AWS_REGION`, `LLM_MODEL`, `EMBEDDING_MODEL` — the values above are empty placeholders; fill in real credentials before running (see the repository for what each one is for).

## Documentation & README

<p align="center">
  <h1 align="center">mcp-ragchat</h1>
  <p align="center">
    An MCP server that adds RAG-powered AI chat to any website. One command from Claude Code.
  </p>
</p>

<p align="center">
  <a href="https://opensource.org/licenses/MIT"><img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="License: MIT"></a>
  <a href="https://nodejs.org/"><img src="https://img.shields.io/badge/node-%3E%3D20-brightgreen.svg" alt="Node.js 20+"></a>
  <a href="https://www.typescriptlang.org/"><img src="https://img.shields.io/badge/TypeScript-5.0-blue.svg" alt="TypeScript"></a>
  <a href="https://modelcontextprotocol.io/"><img src="https://img.shields.io/badge/MCP-compatible-8A2BE2.svg" alt="MCP"></a>
  <a href="https://www.npmjs.com/package/mcp-ragchat"><img src="https://img.shields.io/npm/v/mcp-ragchat.svg" alt="npm"></a>
</p>

---

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**

```bash
git clone https://github.com/gogabrielordonez/mcp-ragchat
cd mcp-ragchat
npm install && npm run build
```

**2. Configure Claude Code** (`~/.claude/mcp.json`)

```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](https://github.com/gogabrielordonez/mcp-ragchat/issues)
- Want to add a feature? Fork, branch, PR.
- Questions? Start a [discussion](https://github.com/gogabrielordonez/mcp-ragchat/discussions)

## Star History

[![Star History Chart](https://api.star-history.com/svg?repos=gogabrielordonez/mcp-ragchat&type=Date)](https://star-history.com/#gogabrielordonez/mcp-ragchat&Date)

---

### Enterprise

Need multi-tenancy, security guardrails, audit trails, and managed infrastructure? Check out [Supersonic](https://adwaizer.com/supersonic) -- the enterprise AI platform built on the same RAG pipeline.

---

**MIT License** -- Gabriel Ordonez