🚀 Agentic RAG with MCP Server 
✨ Overview
Agentic RAG with MCP Server is a powerful project that brings together an MCP (Model Context Protocol) server and client for building Agentic RAG (Retrieval-Augmented Generation) applications.
This setup empowers your RAG system with advanced tools such as:
- 🕵️♂️ Entity Extraction
- 🔍 Query Refinement
- ✅ Relevance Checking
The server hosts these intelligent tools, while the client shows how to seamlessly connect and utilize them.
🖥️ Server — server.py
Powered by the FastMCP class from the mcp library, the server exposes these handy tools:
| Tool Name | Description | Icon |
|---|---|---|
get_time_with_prefix |
Returns the current date & time | ⏰ |
extract_entities_tool |
Uses OpenAI to extract entities from a query — enhancing document retrieval relevance | 🧠 |
refine_query_tool |
Improves the quality of user queries with OpenAI-powered refinement | ✨ |
check_relevance |
Filters out irrelevant content by checking chunk relevance with an LLM | ✅ |
🤝 Client — mcp-client.py
The client demonstrates how to connect and interact with the MCP server:
- Establish a connection with
ClientSessionfrom themcplibrary - List all available server tools
- Call any tool with custom arguments
- Process queries leveraging OpenAI or Gemini and MCP tools in tandem
⚙️ Requirements
- Python 3.9 or higher
openaiPython packagemcplibrarypython-dotenvfor environment variable management
🛠️ Installation Guide
# Step 1: Clone the repository
git clone https://github.com/ashishpatel26/Agentic-RAG-with-MCP-Server.git
# Step 2: Navigate into the project directory
cd Agentic-RAG-with-MCP-Serve
# Step 3: Install dependencies
pip install -r requirements.txt
🔐 Configuration
- Create a
.envfile (use.env.sampleas a template) - Set your OpenAI model in
.env:
OPENAI_MODEL_NAME="your-model-name-here"
GEMINI_API_KEY="your-model-name-here"
🚀 How to Use
- Start the MCP server:
python server.py
- Run the MCP client:
python mcp-client.py
📜 License
This project is licensed under the MIT License.
Thanks for Reading 🙏
1 maintainer
Metadata
1 maintainer
Release files for iflow-mcp_Agentic-RAG-with-MCP-Server-1 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| iflow_mcp_agentic_rag_with_mcp_server_1-0.1.0.tar.gz | 3.0 kB | Without provenance | Details for iflow_mcp_agentic_rag_with_mcp_server_1-0.1.0.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| iflow_mcp_agentic_rag_with_mcp_server_1-0.1.0-py3-none-any.whl | Python 3 | none | any | Details for iflow_mcp_agentic_rag_with_mcp_server_1-0.1.0-py3-none-any.whl |
Total release size: 6.2 kB
Release files / iflow_mcp_agentic_rag_with_mcp_server_1-0.1.0.tar.gz
| Download URL | iflow_mcp_agentic_rag_with_mcp_server_1-0.1.0.tar.gz |
|---|---|
| Size | 3.0 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
1043c558d7e9ea58b1117e22ed3eea5834773566b44c8d86ef5d718a0742fcb0
|
|
BLAKE2b-256 checksum How to use checksums |
86ffded59086ebc7b6307b62e57580df19087cc584195f19a1f50ec75de62afd
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
uv/0.9.10 {"installer":{"name":"uv","version":"0.9.10"},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"macOS","version":null,"id":null,"libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":null}
|
Release files / iflow_mcp_agentic_rag_with_mcp_server_1-0.1.0-py3-none-any.whl
| Download URL | iflow_mcp_agentic_rag_with_mcp_server_1-0.1.0-py3-none-any.whl |
|---|---|
| Size | 3.2 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
3851bfe7605ad2c9bab22a9aa66a20c78c0f38be43e1dc3a70d5b03a38135679
|
|
BLAKE2b-256 checksum How to use checksums |
5adef665bd089b4f57021dff962663d20ef682eb60308642fa28a88b9081b905
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
uv/0.9.10 {"installer":{"name":"uv","version":"0.9.10"},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"macOS","version":null,"id":null,"libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":null}
|
Security for iflow-mcp_Agentic-RAG-with-MCP-Server-1 0.1.0
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
No provenance found
None of the files in this release were uploaded with provenance metadata.
A note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
