analytics-data

Kayzen Analytics

by springwq

Kayzen Analytics integrates marketing analytics with powerful tools for campaign analysis, reporting, and performance op

Integrates with Kayzen Analytics API to access and analyze marketing campaign data, providing tools for report listing, data fetching with date filtering, and performance optimization insights.

github stars

1

Automated token managementTypeScript implementationDate range filtering

best for

  • / Digital marketers analyzing ad campaign performance
  • / Data analysts working with advertising metrics
  • / Marketing teams optimizing campaign strategies

capabilities

  • / List available Kayzen analytics reports
  • / Fetch report results with date filtering
  • / Analyze campaign performance data
  • / Access advertising metrics and insights
  • / Manage authentication tokens automatically

what it does

Connects to Kayzen Analytics API to retrieve and analyze advertising campaign data and performance metrics.

about

Kayzen Analytics is a community-built MCP server published by springwq that provides AI assistants with tools and capabilities via the Model Context Protocol. Kayzen Analytics integrates marketing analytics with powerful tools for campaign analysis, reporting, and performance op It is categorized under analytics data.

how to install

You can install Kayzen Analytics in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

MIT

Kayzen Analytics is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

Kayzen Analytics MCP Server

A Model Context Protocol (MCP) server implementation for interacting with Kayzen Analytics API. This package enables AI models to access and analyze Kayzen advertising campaign data through a standardized interface.

Features

  • Automated Authentication: Built-in token management with automatic refresh mechanism
  • Report Management: Easy access to Kayzen analytics reports
  • Error Handling: Comprehensive error handling for API interactions
  • TypeScript Support: Full TypeScript implementation with type definitions
  • Environment Based Configuration: Simple setup using environment variables

Installation

npm install @feedmob-ai/kayzen-mcp

Configuration

Create a .env file with your Kayzen credentials:

KAYZEN_USERNAME=your_username
KAYZEN_PASSWORD=your_password
KAYZEN_BASIC_AUTH=your_basic_auth_token
KAYZEN_BASE_URL=https://api.kayzen.io/v1  # Optional, defaults to this value

Usage

Basic Setup

import { KayzenMCPServer } from '@feedmob-ai/kayzen-mcp';

const server = new KayzenMCPServer();
server.start();

Available Tools

1. list_reports

Lists all available reports from Kayzen Analytics.

  • Inputs: None
  • Returns: Array of report objects containing:
    • id: Report identifier
    • name: Report name
    • type: Report type
const reports = await server.tools.list_reports();

2. get_report_results

Retrieves results for a specific report.

  • Inputs:
    • report_id (string, required): ID of the report to fetch
    • start_date (string, optional): Start date in YYYY-MM-DD format
    • end_date (string, optional): End date in YYYY-MM-DD format
  • Returns: Report data and metadata
const results = await server.tools.get_report_results({
  report_id: 'report_id',
  start_date: '2024-01-01',  // optional
  end_date: '2024-01-31'     // optional
});

3. analyze_report_results (Prompt)

Analyzes report results and provides insights.

  • Inputs:
    • report_id (string): ID of the report to analyze
  • Analysis includes:
    • Performance metrics
    • Key trends
    • Areas for optimization
    • Unusual patterns or anomalies

Setup

Usage with Claude Desktop

To use this with Claude Desktop, add the following to your claude_desktop_config.json:

NPX

{
  "mcpServers": {
    "github": {
      "command": "npx",
      "args": [
        "-y",
        "@feedmob-ai/kayzen-mcp"
      ],
      "env": {
        "KAYZEN_USERNAME": "username",
        "KAYZEN_PASSWORD": "pasword",
        "KAYZEN_BASIC_AUTH": "auth token"
      }
    }
  }
}

Development

Prerequisites

  • Node.js (v16 or higher)
  • npm (v7 or higher)
  • Kayzen API credentials

Scripts

# Install dependencies
npm install

# Build the project
npm run build

# Start the server
npm start

# Development mode with hot-reload
npm run dev

Project Structure

kayzen-mcp/
├── src/
│   ├── server.ts        # MCP server implementation
│   └── kayzen-client.ts # Kayzen API client
├── dist/               # Compiled JavaScript
└── package.json       # Project configuration

Dependencies

Main dependencies:

  • @modelcontextprotocol/sdk: ^1.7.0
  • axios: ^1.8.3
  • dotenv: ^16.4.7
  • zod: ^3.24.2

Error Handling

The server handles various error scenarios:

  • Authentication failures
  • Invalid API requests
  • Network issues
  • Token expiration and refresh
  • Invalid parameters

License

MIT License

Author

FeedMob

FAQ

What is the Kayzen Analytics MCP server?
Kayzen Analytics is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for Kayzen Analytics?
This profile displays 10 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 out of 5—verify behavior in your own environment before production use.
MCP server reviews

Ratings

4.510 reviews
  • Shikha Mishra· Oct 10, 2024

    Kayzen Analytics is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Piyush G· Sep 9, 2024

    We evaluated Kayzen Analytics against two servers with overlapping tools; this profile had the clearer scope statement.

  • Chaitanya Patil· Aug 8, 2024

    Useful MCP listing: Kayzen Analytics is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Sakshi Patil· Jul 7, 2024

    Kayzen Analytics reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Ganesh Mohane· Jun 6, 2024

    I recommend Kayzen Analytics for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Oshnikdeep· May 5, 2024

    Strong directory entry: Kayzen Analytics surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Dhruvi Jain· Apr 4, 2024

    Kayzen Analytics has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Rahul Santra· Mar 3, 2024

    According to our notes, Kayzen Analytics benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Pratham Ware· Feb 2, 2024

    We wired Kayzen Analytics into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Yash Thakker· Jan 1, 2024

    Kayzen Analytics is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.