Tiny Chat
Click on "Install 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., "@Tiny ChatSearch the knowledge base for information on API rate limits"
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.
Tiny Chat
Installation
Tested with Python 3.10 or later
Development Installation
pip install -r requirements.txtPackage Installation
# Build the package
pip install build
python -m build
# Install the built package
pip install dist/*.whlRelated MCP server: RAG MCP Server
Web Interface Usage
Running from source (development)
streamlit run tiny_chat/main.py --server.address=127.0.0.1only database (development)
streamlit run tiny_chat/main.py --server.address=127.0.0.1 -- --databaseRunning installed package
tiny-chatonly database
tiny-chat --database
MCP Usage
Claude Desktop example.
{
"mcpServers": {
"tiny-chat": {
"command": "/path/to/tiny_chat/.venv/bin/tiny-chat-mcp",
"env": {
"DB_CONFIG": "/path/to/tiny_chat/database_config.json"
}
}
}
}OpenAI Chat API RAG Server Usage
tiny-chat-apimodel: target search qdrant collection name (model change in conversation).
curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{"model": "qdrant-collection-name", "messages": [{"role": "user", "content": "カレーライスの材料は?"}]}'Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Tools
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- Flicense-qualityCmaintenanceAn enterprise-ready MCP server that exposes a RAG tool for retrieving relevant context and metadata from a Qdrant vector database using natural language queries.2
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MCP directory API
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curl -X GET 'https://glama.ai/api/mcp/v1/servers/to-aoki/tiny_chat'
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