Where is the vector data stored?

The server stores domain configuration and embedding vectors locally under `~/.mcp-ragchat/domains/`.

Which providers can generate chat responses?

The README lists OpenAI, Anthropic, and Google Gemini for chat completions.

Which embedding providers are supported?

The README lists OpenAI, Google Gemini, and AWS Bedrock embeddings.

Does the widget require a frontend framework?

No. The generated widget is a self-contained script with no dependencies, frameworks, or build step.

MCP Ragchat

ragchatembeddingswebsitelocal

Adds local RAG chat, an HTTP server, and an embeddable widget to websites using markdown content and an LLM API key.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

One-click editor setup isn’t available for this listing yet — we don’t have a confirmed install command, and we’d rather show nothing than point your editor at the wrong package or host. Follow the project’s own setup instructions, linked above.

Manual Client & Custom JSON ConfigExpand JSON ▾

No confirmed setup config for this listing yet. We only publish a config block when the install details come from the project itself — its README, its docs, or a verified owner. We haven’t found those for gogabrielordonez/mcp-ragchat, and we’d rather show nothing than a guess you’d paste into your client. Follow the project’s own setup instructions for the current steps.

Overview

This MCP server turns markdown sections into searchable documents with locally stored vector embeddings. It can test retrieval and responses, start a local chat server, and generate a dependency-free widget script for embedding on a website. Use it when you want website chat without managing cloud infrastructure or a separate database.

Use cases

Seed a website knowledge base from FAQ or documentation markdown

Test RAG retrieval and generated answers before deployment

Start a local HTTP chat endpoint for a website

Generate a floating chat widget script

Review configured domains and document counts

Key features

Markdown section-based knowledge-base setup

Local JSON vector store with cosine similarity search

OpenAI, Anthropic, Gemini, and AWS Bedrock provider support

Local HTTP chat server with CORS and input sanitization

Dependency-free embeddable widget generation

Domain status and document-count reporting

Capabilities & Tool Schemas (5)Self-reported

Inspect callable tools, capabilities, and parameters exposed to AI agents by MCP Ragchat.

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.

Documentation Overview

mcp-ragchat

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

Code

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)

config.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

Code

                        +------------------+
                        |  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

Code

~/.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

Read the full README →View source on GitHub →

Related MCP Servers

View all in end to end RAG platforms View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars

Stargazers on the source repository.

Last commit

6mo ago

Most recent push to the default branch.

Tools exposed

Callable tools this server registers over MCP.

Directory activity

2 views

Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about MCP Ragchat

It accepts markdown content and turns each level-two heading section, beginning with `##`, into a searchable document.

AllMCPs Directory Badge

Full Badge Customizer

Showcase your server listing on GitHub or your project documentation. Embed this dynamic SVG badge to highlight official listing status and live engagement.

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Markdown (GitHub README)

[![AllMCPs](https://allmcps.com/api/badge/gogabrielordonez-mcp-ragchat?style=directory)](https://allmcps.com/mcp/gogabrielordonez-mcp-ragchat)

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Technical Specs & Signals

Categoryend to end RAG platforms

PricingBring your own API key (usage-based cost)

10/10 checks healthy over the last 31d

45Quality signal: Fair · 45/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools23/30

Adoption & activity1/15

Community engagement0/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

★ Featured

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MCP & Agent Skills for Automated Documentation, and codebase conventions + context

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Explore more

More in 🔎 end to end RAG platforms →Alternatives to MCP Ragchat →Install in Claude Desktop Install in Cursor Install in VS Code

MCP Ragchat

ragchatembeddingswebsitelocal

Adds local RAG chat, an HTTP server, and an embeddable widget to websites using markdown content and an LLM API key.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Manual Client & Custom JSON ConfigExpand JSON ▾

Overview

Use cases

Seed a website knowledge base from FAQ or documentation markdown

Test RAG retrieval and generated answers before deployment

Start a local HTTP chat endpoint for a website

Generate a floating chat widget script

Review configured domains and document counts

Key features

Markdown section-based knowledge-base setup

Local JSON vector store with cosine similarity search

OpenAI, Anthropic, Gemini, and AWS Bedrock provider support

Local HTTP chat server with CORS and input sanitization

Dependency-free embeddable widget generation

Domain status and document-count reporting

Capabilities & Tool Schemas (5)Self-reported

Inspect callable tools, capabilities, and parameters exposed to AI agents by MCP Ragchat.

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.

Documentation Overview

mcp-ragchat

An MCP server that adds RAG-powered AI chat to any website. One command from Claude Code.

Demo

Code

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)

config.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

Code

                        +------------------+
                        |  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

Code

~/.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

Read the full README →View source on GitHub →

Related MCP Servers

View all in end to end RAG platforms View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars

Stargazers on the source repository.

Last commit

6mo ago

Most recent push to the default branch.

Tools exposed

Callable tools this server registers over MCP.

Directory activity

2 views

Config copies, upvotes, and views on AllMCPs.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about MCP Ragchat

It accepts markdown content and turns each level-two heading section, beginning with `##`, into a searchable document.

AllMCPs Directory Badge

Full Badge Customizer

Showcase your server listing on GitHub or your project documentation. Embed this dynamic SVG badge to highlight official listing status and live engagement.

Badge Style:

Live Dynamic SVG Preview

Markdown (GitHub README)

[![AllMCPs](https://allmcps.com/api/badge/gogabrielordonez-mcp-ragchat?style=directory)](https://allmcps.com/mcp/gogabrielordonez-mcp-ragchat)

HTML Embed

<a href="https://allmcps.com/mcp/gogabrielordonez-mcp-ragchat"><img src="https://allmcps.com/api/badge/gogabrielordonez-mcp-ragchat?style=directory" alt="MCP Ragchat on AllMCPs" /></a>

Technical Specs & Signals

Categoryend to end RAG platforms

PricingBring your own API key (usage-based cost)

10/10 checks healthy over the last 31d

45Quality signal: Fair · 45/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools23/30

Adoption & activity1/15

Community engagement0/10

★ Featured

Moxie Docs MCP

MCP & Agent Skills for Automated Documentation, and codebase conventions + context

Explore Server →

Own this project?

Free dofollow backlink: add your website and place the AllMCPs badge on it — no claim needed. We detect it automatically and keep it verified as long as the badge stays live.

Claim & get free dofollow

Share & Embed

Add our SVG badge (dark/light directory styles) or embeddable widget to your site.

Explore more

More in 🔎 end to end RAG platforms →Alternatives to MCP Ragchat →Install in Claude Desktop Install in Cursor Install in VS Code