K2view MCP Server

What is this?

K2view MCP Server is an enterprise-grade, real-time data delivery platform that unifies and virtualizes multi-source data for AI agents, large language models, and analytics systems. It bridges siloed data stores such as operational databases, data warehouses, and third-party APIs to provide low-latency, secure, and granular access without heavy ETL or full data replication. By implementing the MCP protocol, it ensures interoperability, transactional consistency, and robust security across heterogeneous environments.

Built on a modular microservices architecture, it features an authentication gateway, a virtualization engine for on-the-fly view composition, a transformation layer for data normalization, and a security module with row- and field-level controls. Change Data Capture streams and audit logs integrate with Kafka, Kinesis, and ELK for observability, while hybrid container deployments on Docker and Kubernetes enable seamless scalability. This makes K2view MCP Server ideal for powering real-time AI-driven applications and analytical workflows.

Quick Start

Install the server using npm:

npm install @modelcontextprotocol/server-k2view-mcp-server

Then add it to your MCP client configuration:

{
  "mcpServers": {
    "k2view-mcp-server": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-k2view-mcp-server"],
      "env": {
        "API_KEY": "your-api-key-here"
      }
    }
  }
}

Key Features

Feature 1: Real-time multi-source federation combines live data from ERP, CRM, and data lakes without ETL, delivering sub-100 ms retrieval for AI and analytics.

Feature 2: Granular security and privacy with row-level and field-level authorization, dynamic masking, and compliance support for GDPR, CCPA, and HIPAA.

Feature 3: Hybrid deployment and scalability across on-premises, Docker, and Kubernetes environments with auto-scaling and cluster sharding for high availability.

Example Usage

In a typical AI-driven customer 360 scenario, a chatbot fetches unified customer profiles and recent orders on-the-fly to provide accurate, up-to-date responses without batch ETL delays.

// Example code
const result = await client.callTool({
  name: "tool-name",
  arguments: {
    param: "value"
  }
});

This code invokes the designated tool on the MCP Server, passing parameters to retrieve and return the requested dataset for downstream processing.

Configuration

The server accepts the following environment variables:

API_KEY – Your API key for authenticating with K2view MCP Server, obtainable from your account settings.

SETTING_1 (optional) – Optional setting to configure the request timeout in milliseconds.

Available Tools/Resources

MCPClient: SDK for JavaScript, Python, and Java to connect, authenticate, and execute queries against the MCP Server.

DataViewBuilder: Utility library for declaratively defining and managing Data Views, joins, and field selection.

Who Should Use This?

This server is perfect for:

Use case 1: AI developers building conversational bots that need unified, real-time customer data.

Use case 2: Data engineers integrating multiple back ends without heavy ETL for analytics pipelines.

Use case 3: DevOps teams deploying scalable, containerized microservices with secure data virtualization.

Conclusion

K2view MCP Server makes accessing live enterprise data for AI and analytics easy, with low-latency delivery, robust security, and seamless scalability. Try it out today to streamline your data workflows and power intelligent applications.

Check out the GitHub repository for more information and to contribute.