MCP AI Server
Provides secure management of API keys and sensitive credentials
Powers the local MCP server to expose tools and functionality to client applications
Repository hosting for the MCP server code, accessible via git clone
Integrates with ChatGPT for intelligent search capabilities, allowing it to leverage knowledge bases for domain-specific queries
Click on "Deploy 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., "@MCP AI Serversearch for recent updates to our API documentation"
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.
🧠MCP AI Server — Modular Context Protocol for Intelligent Search
Welcome to the MCP AI Server, a powerful and modular tool that uses RAG-based retrieval, Pinecone vector storage, and MCP (Model Context Protocol) to create intelligent assistants capable of answering domain-specific questions from your own knowledge base.
🚀 Features
✅ Local MCP server with FastAPI + Claude/ChatGPT integration
✅ Embedding using intfloat/multilingual-e5-large (via SentenceTransformer)
✅ Fast vector search with Pinecone
✅ Documented tools exposed to clients like Claude and Cursor IDE
✅ Secure .env usage for managing API keys
✅ Clean, extensible architecture
Related MCP server: lsfusion-mcp
🔧 Setup Instructions
1. Clone the Repo
git clone git@github.com:MeetRathodNitsan/MCP1.git
cd MCP12. Create a Virtual Environment
python -m venv .venv
# Windows
.venv\Scripts\activate
# macOS/Linux
source .venv/bin/activate3. Install Dependencies
pip install -r requirements.txt4. Configure Environment Variables
OPENAI_API_KEY=your-api-key...
PINECONE_API_KEY=...
PINECONE_ENVIRONMENT=your-env5. How to use it
uv --directory F:/Project run main.pyThis server cannot be deployed
Maintenance
Related MCP Connectors
Search your knowledge bases from any AI assistant using hybrid RAG.
Ingest, manage, and retrieve documents for RAG-powered AI applications
Cloud or self-hosted knowledge for AI agents: hybrid search, reranking, GraphRAG, scoped MCP tools.
Persistent memory and knowledge management for AI agents with semantic search and 50+ tools.
Related MCP Servers
- FlicenseNot gradedqualityNot gradedmaintenanceEnables AI assistants to perform semantic search, manage vectors, and interact with Pinecone vector databases through standardized MCP tools. Supports querying, upserting, deleting vectors and monitoring database statistics for knowledge base operations.4 npm-

lsfusion-mcpofficial
FlicenseNot gradedqualityBmaintenanceEnables RAG-powered documentation search using OpenAI embeddings and Pinecone vector database. Provides an extensible framework for adding additional tools with support for both local STDIO and production HTTP transports.1-- AlicenseNot gradedqualityCmaintenanceEnables AI assistants to search and retrieve information from your knowledge base using RAG (Retrieval-Augmented Generation) with hybrid search, document indexing, and ChromaDB vector storage.25 npmMIT
- FlicenseNot gradedqualityDmaintenanceEnables searching and asking questions over your PDF documents using a Pinecone vector index with local embeddings and language model, no external LLM API key required.-