README.md

metadata

title: Deploy Ready Copilot
emoji: 🚀
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.49.1
app_file: app.py
pinned: false
tags:
  - mcp-in-action-track-enterprise
  - building-mcp-track-enterprise
  - gradio
  - claude
  - multi-agent
  - deployment
  - productivity
  - context7
  - github
  - vercel
  - mcp-server

🚀 Deployment Readiness Copilot

Multi-agent AI system for deployment readiness validation and documentation generation

🎯 Overview

The Deployment Readiness Copilot is a productivity-focused, developer-centric tool that automates deployment readiness checks using a multi-agent architecture. It combines Claude's reasoning with sponsor LLM cross-checks and MCP tool integration to provide comprehensive pre-deployment validation across multiple platforms.

✨ Features

• 🤖 Multi-Agent Pipeline: Planner → Evidence Gatherer → Synthesis → Documentation → Reviewer → Docs Lookup → Deployment
• 📁 Codebase Analysis: Upload folder (ZIP) or GitHub repo → Auto-detect framework, dependencies, configs
• 📚 Context7 Documentation Integration: Automatic framework/platform documentation lookups
• 🔧 MCP Tool Integration: Real-time deployment signals from Hugging Face Spaces, Vercel, Context7, and GitHub
• 🚀 Multi-Platform Deployment: Deploy to Vercel, Netlify, AWS, GCP, Azure, Railway, Render, Fly.io, Kubernetes, Docker
• 🎓 Sponsor LLM Cross-Checks: Gemini 2.0 Flash + OpenAI GPT-4o mini for synthesis and validation
• 📝 Auto-Documentation: Generates changelog entries, README snippets, and announcement drafts
• ✅ Risk Assessment: Automated review with confidence scoring and actionable findings

🔥 10 Major Utility Improvements

1. 📊 Real-Time Deployment Monitoring: Track deployment status, stages, and logs in real-time
2. 🔒 Security Scanning: Scan dependencies for vulnerabilities and detect exposed secrets
3. 💰 Cost Estimation: Compare costs across platforms with optimization recommendations
4. 🔐 Environment Variable Validation: Validate env vars, detect missing required vars, security issues
5. ⚡ Performance Optimization: Framework-specific performance analysis and improvement suggestions
6. 🔄 CI/CD Pipeline Generation: Auto-generate GitHub Actions and GitLab CI configurations
7. ⏮️ Rollback Strategies: Generate rollback plans and disaster recovery procedures
8. 🌍 Multi-Environment Support: Deploy to dev/staging/production with environment-specific configs
9. 👥 Team Collaboration: Review sessions, stakeholder approvals, comments, and feedback
10. 📊 Monitoring Integration: Setup recommendations for Sentry, New Relic, Datadog, and platform-native tools

🏗️ Architecture

Agents

1. Planner Agent (Claude): Analyzes project context and generates deployment readiness checklist
2. Evidence Agent (Claude + MCP): Gathers real deployment signals via MCP tools
3. Synthesis Agent (Gemini/OpenAI): Cross-validates Claude's evidence to earn sponsor bonus points
4. Documentation Agent (Claude): Generates deployment communications
5. Reviewer Agent (Claude): Final risk assessment with confidence scoring
6. Documentation Lookup Agent (Context7): Looks up framework/platform docs for:
   • Deployment guides
   • Dependency compatibility
   • Config validation
   • Runbook generation
   • Environment variables
   • Migration guides
   • Observability setup
7. Deployment Agent (GitHub): Prepares and executes deployment actions

MCP Tools Used

• Context7: Framework/platform documentation lookups
• Hugging Face Spaces: Status checks and validation
• Vercel: Deployment validation
• GitHub: Deployment PR creation and workflow triggers
• (Extensible to other MCP-compatible services)

🚀 Quick Start

1. Set Environment Variables (in HF Space Secrets):

   • ANTHROPIC_API_KEY: Your Claude API key (required)
   • GOOGLE_API_KEY or GEMINI_API_KEY: Enables Gemini sponsor synthesis
   • OPENAI_API_KEY: Enables OpenAI sponsor synthesis
   • SPONSOR_LLM_PRIORITY: Optional override (default gemini,openai)
   • GEMINI_MODEL, OPENAI_MODEL: Optional model overrides
   • HF_TOKEN: For Hugging Face MCP tools (optional)
   • GITHUB_TOKEN: For GitHub deployment actions (optional)
   • GITHUB_REPO: Repository in format owner/repo (optional, for deployments)
   • GITHUB_BRANCH: Branch name (default: main) (optional)

2. Upload & Analyze:

   • Upload your project folder as ZIP, OR
   • Enter GitHub repo URL
   • Click "Analyze Codebase" to auto-detect framework and dependencies

3. Configure & Deploy:

   • Review auto-filled project details
   • Select deployment platform (Vercel, AWS, etc.)
   • Choose environment (dev/staging/production)
   • Click "Run Full Pipeline & Deploy"
   • Review all utility reports (security, cost, performance, etc.)
   • Deploy directly via MCP

📋 Example Usage

Project: Telemetry API
Release Goal: Enable adaptive sampling
Code Summary: Adds config surface, toggles feature flag, bumps schema version.
Stakeholders: eng, sre

The system will:

1. Generate a deployment readiness plan
2. Gather evidence via MCP tools
3. Lookup framework/platform documentation via Context7
4. Cross-validate evidence with sponsor LLMs
5. Create documentation artifacts
6. Prepare GitHub deployment actions (if configured)
7. Provide final review with risk assessment

🎯 Hackathon Submission

Primary Tracks:

• mcp-in-action-track-enterprise (MCP in Action - Enterprise)
• building-mcp-track-enterprise (Building MCP - Enterprise)

Multi-Track Compatibility:

• ✅ Track 1 (Building MCP): Functions as an MCP server with custom tools.
• ✅ Track 2 (MCP in Action): Autonomous multi-agent behavior with planning, reasoning, and execution via MCP tools.
• ✅ LlamaIndex Prize: Integrated RAGAgent using LlamaIndex for codebase analysis.
• ✅ OpenAI/Gemini Prizes: Uses OpenAI and Gemini for sponsor cross-checks.

Key Highlights:

• ✅ Autonomous multi-agent behavior with planning, reasoning, and execution
• ✅ MCP servers used as tools (Context7, HF Spaces, Vercel, GitHub)
• ✅ Context7 integration for comprehensive documentation lookups
• ✅ GitHub deployment actions for direct deployment execution
• ✅ Gradio app with MCP server support (mcp_server=True)
• ✅ Sponsor LLM integration (Gemini, OpenAI) with configurable priority
• ✅ Real-world productivity use case for developers
• ✅ 10 utility improvements covering security, cost, performance, CI/CD, monitoring, and collaboration

🔧 Technical Stack

• Gradio 5.49.1: UI framework with MCP server support
• Anthropic Claude 3.5 Sonnet: Primary reasoning engine
• Google Gemini 2.0 Flash: Sponsor cross-validation
• OpenAI GPT-4o mini: Alternate sponsor cross-validation
• Hugging Face Hub: MCP client for tool integration
• Context7 MCP: Documentation lookup service
• GitHub & Vercel MCP: Deployment validation and workflow triggers
• LlamaIndex: RAG engine for codebase analysis
• Python 3.10+: Core runtime

🔐 Secrets & API Keys

Add secrets in Hugging Face Space → Settings → Repository secrets:

Secret	Purpose
ANTHROPIC_API_KEY	Required for Claude agents
GOOGLE_API_KEY / GEMINI_API_KEY	Enable Gemini sponsor synthesis
OPENAI_API_KEY	Enable OpenAI sponsor synthesis
SPONSOR_LLM_PRIORITY	Optional ordering, e.g. gemini,openai
GEMINI_MODEL, OPENAI_MODEL	Optional model overrides
HF_TOKEN	Optional Hugging Face MCP access
GITHUB_TOKEN, GITHUB_REPO, GITHUB_BRANCH	GitHub deployment actions

📝 License

MIT License

🔗 Social Media

[Link to your social media post about the project]

🎥 Demo Video

[Link to your demo video]

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Built for MCP's 1st Birthday Hackathon 🎉