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.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
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 identifiername: Report nametype: 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 fetchstart_date(string, optional): Start date in YYYY-MM-DD formatend_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: ^1.7.0axios: ^1.8.3dotenv: ^16.4.7zod: ^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 37 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.6 out of 5—verify behavior in your own environment before production use.
Use Cases▌
Extended AI Capabilities
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
Context Enhancement
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Workflow Automation
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
Implementation Guide▌
Prerequisites
- ›Claude Desktop 0.7.0+ or Cursor IDE with MCP support
- ›Basic understanding of MCP architecture and capabilities
- ›Access credentials for integrated services (if required)
- ›Willingness to experiment and iterate on configuration
Time Estimate
15-60 minutes depending on server complexity
Installation Steps
- 1.Install MCP server: npm install -g [package-name] or via GitHub
- 2.Add server configuration to ~/.claude/mcp.json
- 3.Provide required credentials and configuration
- 4.Restart Claude Desktop to load new server
- 5.Test basic functionality with simple prompts
- 6.Explore capabilities and experiment with use cases
- 7.Document successful patterns for reuse
Troubleshooting
- ⚠MCP server not loading: Check config syntax, verify installation
- ⚠Connection errors: Check network, firewall, credentials
- ⚠Feature not working: Read server docs, check required parameters
- ⚠Performance issues: Monitor resource usage, check for network latency
- ⚠Conflicts with other servers: Check port assignments, namespace collisions
Best Practices▌
✓ Do
- +Read server documentation thoroughly before setup
- +Start with simple use cases to validate functionality
- +Test in non-production environment first
- +Monitor resource usage and performance
- +Keep servers updated for bug fixes and new features
- +Document configuration for team members
- +Use environment variables for sensitive configuration
✗ Don't
- −Don't grant overly permissive access to MCP servers
- −Don't skip reading security considerations in docs
- −Don't expose sensitive data without proper controls
- −Don't run untrusted MCP servers without code review
- −Don't ignore error messages—investigate root cause
💡 Pro Tips
- ★Combine multiple MCP servers for powerful workflows
- ★Create custom MCP servers for your specific needs
- ★Share successful configurations with team
- ★Use MCP inspector for debugging
- ★Join MCP community for tips and troubleshooting
Technical Details▌
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
- Model Context Protocol (MCP)
- JSON-RPC 2.0
- stdio or HTTP transport
Compatibility
- Claude Desktop
- Cursor IDE
- Custom MCP clients
When to Use This▌
✓ 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.
Integration▌
- →Tool composition: Chain multiple MCP tools in workflows
- →Context augmentation: Provide AI with relevant external data
- →Action delegation: Let AI execute tasks on external systems
- →Bidirectional sync: Keep AI context and external systems in sync
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
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Ratings
4.6★★★★★37 reviews- ★★★★★Ava Tandon· Dec 20, 2024
Kayzen Analytics is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Chaitanya Patil· Dec 16, 2024
According to our notes, Kayzen Analytics benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Ren Desai· Dec 8, 2024
According to our notes, Kayzen Analytics benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Isabella Sanchez· Nov 27, 2024
We wired Kayzen Analytics into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Noor Iyer· Nov 23, 2024
I recommend Kayzen Analytics for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Ren Abebe· Nov 11, 2024
Kayzen Analytics reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Piyush G· Nov 7, 2024
We wired Kayzen Analytics into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Rahul Santra· Nov 3, 2024
I recommend Kayzen Analytics for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Shikha Mishra· Oct 26, 2024
Kayzen Analytics is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Pratham Ware· Oct 22, 2024
Strong directory entry: Kayzen Analytics surfaces stars and publisher context so we could sanity-check maintenance before adopting.
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