About St RAG MCP
St RAG MCP is an MCP server published by digital-duck in the AI category: mCP streamlit client with RAG support for tool search. It has been installed 0 times through Conduid.
The repository has 12 stars and 4 forks, with the last commit a year ago. Six months or more without a commit doesn't mean the server is broken, but check the open issues (0) before depending on it in production.
Install
npx st-rag-mcpThis server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.
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README
RAG MCP
The RAG system is particularly well-designed, using sentence transformers to build rich contextual embeddings and performing semantic search to find the most relevant tools for each query. This makes the system much more robust than traditional keyword-based approaches.
What is MCP
MCP stands for Model Context protocol, it helps provide contexts to LLM model for tool-use.
Architecture Diagram
MCP with RAG
The Mermaid diagram illustrates:
- 7 Distinct Layers with color coding for easy understanding
- Complete Data Flow from user input through RAG processing to tool execution
- Decision Tree Logic showing how queries are routed through different parsing modes
- RAG Integration highlighting how semantic search enhances tool selection
- Multi-LLM Support showing integration with Google, OpenAI, and Anthropic
- MCP Server Integration with tool discovery and execution
- Data Persistence including SQLite storage and caching mechanisms
Key Features
- Intelligent Tool Selection using RAG to find relevant tools dynamically
- Flexible Parsing Strategy with fallback mechanisms (RAG → LLM → Rule-based)
- Comprehensive Logging for debugging and performance analysis
- Caching Strategy for efficient server discovery
- Multi-Provider LLM Support for flexibility and redundancy
Setup
# create a virtual environment
conda create -n mcp
conda activate mcp
# obtain source code
git clone https://github.com/digital-duck/st_rag_mcp.git
cd st_rag_mcp
pip install -r requirements.txt
# open 1st terminal
cd src
python mcp_server.py
# in 2nd terminal
conda activate mcp
cd src
streamlit run mcp_client.py
Demo Video
References
README mirrored from the source repository 3 months ago. The original is authoritative.