Vignan University MCP Server

A FastAPI-based Model Context Protocol (MCP) server that enables semantic search over the Vignan University knowledge base using Pinecone vector storage and Sentence Transformers.

Overview

This server exposes a simple tool interface that allows clients to retrieve semantically relevant chunks of information from the Vignan University namespace stored in Pinecone. It uses the all-MiniLM-L6-v2 sentence transformer model to embed queries and perform similarity search.

Related MCP server: Pinecone MCP Server

Prerequisites

• Python 3.8+

• A Pinecone account with an index populated under the Vignan namespace

• The index must use 384-dimensional vectors (matching all-MiniLM-L6-v2 output)

Installation

1. Clone the repository and navigate to the project directory.

2. Install dependencies:

   pip install -r requirements.txt

3. Set up environment variables by creating a .env file in the project root:

   PINECONE_API_KEY=your_pinecone_api_key
   PINECONE_INDEX=your_index_name

Running the Server

python vignan_mcp_server.py

The server will start at http://localhost:8000.

API Endpoints

GET /list-tools

Returns metadata about all available tools exposed by this MCP server.

Response:

{
  "server": "VignanUniversity MCP Server",
  "tools": [
    {
      "name": "VignanUniversity",
      "description": "...",
      "parameters": { ... }
    }
  ]
}

POST /callTool

Invokes a tool by name with the provided arguments.

Request body:

{
  "name": "VignanUniversity",
  "arguments": {
    "query": "query",
    "top_k": 5
  }
}

Response:

{
  "result": [
    {
      "score": 0.91,
      "text": "Relevant chunk text...",
      "source": "document_name.pdf",
      "chunk_index": 3
    }
  ]
}

GET /health

Health check endpoint.

Response:

{ "status": "healthy" }

Project Structure

.
├── vignan_mcp_server.py   # Main server application
├── requirements.txt       # Python dependencies
└── .env                   # Environment variables

Dependencies