MCP

Introduction

The Model Context Protocol (MCP) 🔌 is a protocol for communicating with LLMs, primarily used for exchanging information (context) or performing actions.

🚀 enVector supports using MCP with various AI applications. You can interact with your LLMs like Claude through natural language inputs.

Use Cases

The enVector MCP server allows your AI application to directly interact with enVector's utilities.

• Secure AI coding assistants

• Secure RAG

• Secure Context Memory

• Or other AI applications needs to be secured

Quick Start

Follow our Quick Start tutorial to run your first secure search or jump right into the detailed MCP guide to set up your environment.

1. Run enVector server.

2. Clone the envector-mcp-server and setting up the Python environment.

   python -m venv .venv
   source .venv/bin/activate
   pip install -r requirements.txt

   See more details in envector-mcp-server manuals.

3. (Optional) Run MCP server with the following example command:

   python srcs/server.py \
       --envector-address "your-ev-address" \
       --envector-cloud-access-token "your-ev-access-token"

   This is optional when you run MCP server via AI application (e.g. Claude Desktop). See more details in Run MCP Server.

4. Connect your AI application (e.g. Claude) to the MCP server by configuring settings and Chat with enVector 🚀

   See more details in Connect MCP Server.

Next Steps

• 🚀 MCP Guide: How to start enVector MCP in details

• 🔌 MCP Integration: How to integrate your AI applications

  • Claude

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  • Gemini

  • Codex

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