documcp

--- documcp: last_updated: "2025-11-20T00:46:21.949Z" last_validated: "2025-11-20T00:46:21.949Z" auto_updated: false update_frequency: monthly --- # How to Use DocuMCP Deployment Analytics This guide shows you how to access and use DocuMCP's built-in deployment analytics to track your documentation deployment success and patterns. ## Quick Setup ```bash # Analyze deployment patterns: "analyze my deployment history and provide insights" ``` ## Analytics Overview DocuMCP provides comprehensive **deployment analytics** to help you understand and optimize your documentation deployment process: ### Analytics Types - **Deployment Success Tracking**: Monitor deployment success/failure rates - **SSG Performance Analytics**: Compare static site generator effectiveness - **Build Time Metrics**: Track deployment speed and optimization opportunities - **Project Pattern Analysis**: Understand which configurations work best ### Built-in Analytics Features - **Deployment Health Scoring**: 0-100 health score for your deployment pipeline - **SSG Comparison**: Compare success rates across different static site generators - **Trend Analysis**: Track deployment patterns over time - **Knowledge Graph Integration**: Learn from deployment history for better recommendations ## Using Deployment Analytics ### Method 1: Generate Full Analytics Report ```bash # Get comprehensive deployment analytics: "analyze my deployments and provide a full report" ``` This will provide: 1. Overall deployment success rates 2. SSG performance comparison 3. Build time analysis 4. Project pattern insights 5. Recommendations for optimization ### Method 2: Specific Analytics Queries #### Get SSG Statistics ```bash # Analyze specific SSG performance: "show me statistics for Docusaurus deployments" ``` #### Compare SSG Performance ```bash # Compare multiple SSGs: "compare deployment success rates between Hugo and Jekyll" ``` #### Get Deployment Health Score ```bash # Check deployment pipeline health: "what is my deployment health score?" ``` #### Analyze Deployment Trends ```bash # View deployment trends over time: "show me deployment trends for the last 30 days" ``` ## Deployment Analytics Examples ### Sample Analytics Report ```typescript // Example deployment analytics report structure { "summary": { "totalProjects": 15, "totalDeployments": 42, "overallSuccessRate": 0.85, "mostUsedSSG": "docusaurus", "mostSuccessfulSSG": "hugo" }, "patterns": [ { "ssg": "docusaurus", "totalDeployments": 18, "successfulDeployments": 16, "failedDeployments": 2, "successRate": 0.89, "averageBuildTime": 45000, "projectCount": 8 } ], "insights": [ { "type": "success", "title": "High Success Rate", "description": "Excellent! 85% of deployments succeed" } ] } ``` ### Health Score Breakdown ```typescript // Example health score analysis { "score": 78, "factors": [ { "name": "Overall Success Rate", "impact": 34, "status": "good" }, { "name": "Active Projects", "impact": 20, "status": "good" }, { "name": "Deployment Activity", "impact": 15, "status": "warning" }, { "name": "SSG Diversity", "impact": 9, "status": "warning" } ] } ``` ### MCP Tool Integration ```typescript // Using the analyze_deployments MCP tool directly import { analyzeDeployments } from "./dist/tools/analyze-deployments.js"; // Get full analytics report const report = await analyzeDeployments({ analysisType: "full_report", }); // Get specific SSG statistics const docusaurusStats = await analyzeDeployments({ analysisType: "ssg_stats", ssg: "docusaurus", }); // Compare multiple SSGs const comparison = await analyzeDeployments({ analysisType: "compare", ssgs: ["hugo", "jekyll", "docusaurus"], }); // Get deployment health score const health = await analyzeDeployments({ analysisType: "health", }); ``` ## Advanced Deployment Analytics ### Deployment Pattern Analysis ```bash # Analyze deployment patterns by technology: "show me deployment success patterns for TypeScript projects" # Analyze by project size: "compare deployment success rates for small vs large projects" # Analyze by team size: "show deployment patterns for different team sizes" ``` ### Knowledge Graph Insights ```bash # Get insights from deployment history: "what SSG works best for React projects based on deployment history?" # Learn from similar projects: "recommend deployment strategy based on similar successful projects" # Analyze failure patterns: "what are the common causes of deployment failures?" ``` ### Trend Analysis ```bash # Analyze deployment trends: "show me deployment success trends over the last 6 months" # Compare time periods: "compare deployment performance between Q3 and Q4" # Identify improvement opportunities: "what deployment metrics have improved recently?" ``` ## Troubleshooting ### Common Issues **Problem**: No deployment data available **Solution**: Deploy at least one project to start collecting analytics data **Problem**: Analytics tool returns empty results **Solution**: Ensure knowledge graph storage directory exists and has proper permissions **Problem**: Health score seems low **Solution**: Review deployment failures and optimize SSG configurations **Problem**: Missing deployment history **Solution**: Check that deployment tracking is enabled in knowledge graph ### Analytics Debugging ```bash # Debug deployment analytics issues: "check my deployment analytics configuration and data availability" ``` ## Best Practices ### Deployment Analytics Guidelines 1. **Regular Deployments**: Deploy frequently to build meaningful analytics data 2. **Track Failures**: Learn from deployment failures to improve success rates 3. **Monitor Trends**: Review analytics weekly to identify patterns 4. **Compare SSGs**: Use analytics to choose the best SSG for each project type 5. **Health Monitoring**: Keep deployment health score above 70 ### Data Quality 1. **Consistent Tracking**: Ensure all deployments are tracked in knowledge graph 2. **Clean Data**: Review and clean up failed deployment records periodically 3. **Regular Analysis**: Run analytics reports monthly to identify trends 4. **Documentation**: Document deployment patterns and insights 5. **Team Sharing**: Share analytics insights with your development team ## Deployment Analytics Tools ### Built-in DocuMCP Analytics - **Deployment success tracking**: Monitor success/failure rates - **SSG performance analysis**: Compare static site generator effectiveness - **Build time metrics**: Track deployment speed and optimization opportunities - **Knowledge graph insights**: Learn from deployment history patterns ### MCP Tools Available - `analyze_deployments`: Generate comprehensive deployment analytics - `deploy_pages`: Track deployment attempts and outcomes - `recommend_ssg`: Get SSG recommendations based on analytics ## Next Steps - [Deploy Pages](../reference/mcp-tools.md#deploy_pages) - [SSG Recommendations](../reference/mcp-tools.md#recommend_ssg) - [Knowledge Graph](../knowledge-graph.md) - [Troubleshooting](troubleshooting.md)