Official description7 sourcesMCP servers8d ago
gogabrielordonez/mcp-ragchat
๐ ๐ - Add RAG-based AI chat to any website: local vector store, OpenAI, Anthropic or Gemini models, aโฆ
View on GitHub โDirect link ยท no redirect page
Official description๐ ๐ - Add RAG-based AI chat to any website: local vector store, OpenAI, Anthropic or Gemini models, a self-contained chat server and an embeddable widget.
Config template (adjust for your client)
claude mcp add mcp-ragchat -- npx -y mcp-ragchat
A pasteable skeleton generated from the repository name. Check the project README for the exact package and arguments.
- Star count
- 2 โ ยท gogabrielordonez/mcp-ragchat ยท read from the GitHub search API ยท (checked 2026-10-16 20:00:20)
- Source
- collected from the punkpeye/awesome-mcp-servers list ยท type: MCP server
- Harvest history
- first seen 2026-10-09 ยท last seen 2026-10-16 ยท seen 32ร ยท listed by 7 sources
- Score
- score 703 ยท 2 โ ยท 7 sources ยท mcp
FAQ (answers come from our records)
How do I install it?
We did not find an install command for this repository (our install coverage is only about 4%). Check its README or releases page.
Who is it for?
We filed it under these tags: MCP ๆๅก. The tags come from the entry itself, not a taxonomy we imposed.
Who maintains it?
repository
gogabrielordonez/mcp-ragchat๏ผfirst catalogued 2026-10-09๏ผlast seen 2026-10-16ใThere is no official certification or quality rating here โ we record sources and dates only.Third-party quality signals
mcp-ragchat
ไปฅไธๆๅฝ่ช่ฏฅไปๅบ็ README๏ผ็ๆฎต๏ผ ยท ๅจ GitHub ่ฏปๅฎๆด README โ
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
Quick Start
**1. Clone and build**
**2. Configure Claude Code** (~/.claude/mcp.json)
**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 tag -- a floating chat bubble, no dependencies. |
| ragchat_status | List all configured domains with document counts and config details. |
How It Works
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
- **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 tag. No frameworks, no build step.
Contributing
Issues and pull requests are welcome.
---
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
Quick Start
**1. Clone and build**
**2. Configure Claude Code** (~/.claude/mcp.json)
**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 tag -- a floating chat bubble, no dependencies. |
| ragchat_status | List all configured domains with document counts and config details. |
How It Works
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
- **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 tag. No frameworks, no build step.
Contributing
Issues and pull requests are welcome.
- Source
- awesome ยท https://github.com/gogabrielordonez/mcp-ragchat
- Browse by field
- MCP servers
- Traction
- 2 โ
- added
- yesterday
More in MCP servers
Filed under
Every tag opens the full list for that filter. Counts are the live catalogue figures.