MCP server
by springwq
Kayzen Analytics integrates marketing analytics with powerful tools for campaign analysis, reporting, and performance op
Connects to Kayzen Analytics API to retrieve and analyze advertising campaign data and performance metrics.
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.
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.
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.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
Kayzen Analytics is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
According to our notes, Kayzen Analytics benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
According to our notes, Kayzen Analytics benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
We wired Kayzen Analytics into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
I recommend Kayzen Analytics for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
Kayzen Analytics reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
We wired Kayzen Analytics into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
I recommend Kayzen Analytics for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
Kayzen Analytics is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Strong directory entry: Kayzen Analytics surfaces stars and publisher context so we could sanity-check maintenance before adopting.
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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.
npm install @feedmob-ai/kayzen-mcp
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
import { KayzenMCPServer } from '@feedmob-ai/kayzen-mcp';
const server = new KayzenMCPServer();
server.start();
list_reportsLists all available reports from Kayzen Analytics.
id: Report identifiername: Report nametype: Report typeconst reports = await server.tools.list_reports();
get_report_resultsRetrieves results for a specific report.
report_id (string, required): ID of the report to fetchstart_date (string, optional): Start date in YYYY-MM-DD formatend_date (string, optional): End date in YYYY-MM-DD formatconst results = await server.tools.get_report_results({
report_id: 'report_id',
start_date: '2024-01-01', // optional
end_date: '2024-01-31' // optional
});
analyze_report_results (Prompt)Analyzes report results and provides insights.
report_id (string): ID of the report to analyzeTo use this with Claude Desktop, add the following to your claude_desktop_config.json:
{
"mcpServers": {
"github": {
"command": "npx",
"args": [
"-y",
"@feedmob-ai/kayzen-mcp"
],
"env": {
"KAYZEN_USERNAME": "username",
"KAYZEN_PASSWORD": "pasword",
"KAYZEN_BASIC_AUTH": "auth token"
}
}
}
}
# Install dependencies
npm install
# Build the project
npm run build
# Start the server
npm start
# Development mode with hot-reload
npm run dev
kayzen-mcp/
├── src/
│ ├── server.ts # MCP server implementation
│ └── kayzen-client.ts # Kayzen API client
├── dist/ # Compiled JavaScript
└── package.json # Project configuration
Main dependencies:
@modelcontextprotocol/sdk: ^1.7.0axios: ^1.8.3dotenv: ^16.4.7zod: ^3.24.2The server handles various error scenarios:
MIT License
FeedMob
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.