Agentic RAG With

Agentic RAG with MCP Server

1# Agentic RAG With

README.mdMCP server

Transport: stdio
Status: Local

Power your agent

Add this server

Install from the publisher. Not a PlugMCP endpoint.

Run on PlugMCP

Host a copy with our keys and logs.

No packaged release in the registry yet. Follow the repository README.

RepositoryPublisher installStep-by-step: add this in Claude

Remote URL or a command on your machine? How the two installs differ

Run on PlugMCP

Host a copy with our keys and logs.

A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.

Launch

Server facts

Local

Transport: stdio
Last checked: 2026-10-28

Stars: 35
Tools: —
Uptime: —

Tools

No tool list yet. Remote servers get one after the first health check.

FAQ

Questions

How do I install Agentic RAG With?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Agentic RAG With need an API key?

No required environment variable is listed on this record.

What transport does Agentic RAG With use?

Agentic RAG With runs locally over stdio.

When was Agentic RAG With last checked?

Last checked 2026-10-28. Agentic RAG with MCP Server

From the publisher

Documentation

🚀 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`

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 ClientSession from the mcp library
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
openai Python package
mcp library
python-dotenv for 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 .env file (use .env.sample as 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 🙏

Agentic RAG With

ashishpatel2635starsClaim this server

Agentic RAG with MCP Server

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Install Run on PlugMCPClaim this server

GitHub

Run on PlugMCP

1# Agentic RAG With

README.mdMCP server

Transport: stdio
Status: Local

Power your agent

Add this server

Install from the publisher. Not a PlugMCP endpoint.

Run on PlugMCP

Host a copy with our keys and logs.

No packaged release in the registry yet. Follow the repository README.

RepositoryPublisher installStep-by-step: add this in Claude

Remote URL or a command on your machine? How the two installs differ

Run on PlugMCP

Host a copy with our keys and logs.

A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.

Launch

Server facts

Local

Transport: stdio
Last checked: 2026-10-28

Stars: 35
Tools: —
Uptime: —

Tools

No tool list yet. Remote servers get one after the first health check.

FAQ

Questions

How do I install Agentic RAG With?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Agentic RAG With need an API key?

No required environment variable is listed on this record.

What transport does Agentic RAG With use?

Agentic RAG With runs locally over stdio.

When was Agentic RAG With last checked?

Last checked 2026-10-28. Agentic RAG with MCP Server

From the publisher

Documentation

🚀 Agentic RAG with MCP Server

✨ Overview

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`

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 ClientSession from the mcp library
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
openai Python package
mcp library
python-dotenv for 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 .env file (use .env.sample as 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 🙏

Add this server

Host a copy with our keys and logs.

Server facts

Tools

Questions

How do I install Agentic RAG With?

Does Agentic RAG With need an API key?

What transport does Agentic RAG With use?

When was Agentic RAG With last checked?

Documentation

🚀 Agentic RAG with MCP Server

✨ Overview

🖥️ Server — server.py

🤝 Client — mcp-client.py

⚙️ Requirements

🛠️ Installation Guide

🔐 Configuration

🚀 How to Use

📜 License

Add this server

Host a copy with our keys and logs.

Server facts

Tools

Questions

How do I install Agentic RAG With?

Does Agentic RAG With need an API key?

What transport does Agentic RAG With use?

When was Agentic RAG With last checked?

Documentation

🚀 Agentic RAG with MCP Server

✨ Overview

🖥️ Server — server.py

🤝 Client — mcp-client.py

⚙️ Requirements

🛠️ Installation Guide

🔐 Configuration

🚀 How to Use

📜 License

🖥️ Server — `server.py`

🤝 Client — `mcp-client.py`

🖥️ Server — `server.py`

🤝 Client — `mcp-client.py`