# Open Source & Publishing Roadmap
**Timeline:** 2 Weeks
**Goal:** Make ProduckAI MCP Server publicly available on GitHub and PyPI
**Status:** Planning Phase
---
## Week 1: Preparation & Security (Days 1-7)
### Day 1-2: Repository Setup & Security Audit ✅
**Tasks:**
- [ ] Create GitHub organization: `https://github.com/produckai`
- [ ] Create repositories:
- [ ] `produckai/mcp-server` (MCP server)
- [ ] `produckai/backend` (FastAPI backend)
- [ ] Run security audit script
- [ ] Remove all hardcoded credentials
- [ ] Add `.env.example` files
- [ ] Review git history for leaked secrets
**Security Audit Script:**
```bash
#!/bin/bash
# security-audit.sh
echo "🔍 Scanning for secrets..."
# Check for common secret patterns
patterns=(
"password"
"secret"
"api_key"
"token"
"credentials"
"private_key"
)
for pattern in "${patterns[@]}"; do
echo "Checking for: $pattern"
git grep -i "$pattern" | grep -v ".env.example" | grep -v "docs/"
done
# Check for sensitive files
echo "🔍 Checking for sensitive files..."
find . -name "*.env" -o -name "credentials.json" -o -name "*.pem" -o -name "*.key"
echo "✅ Security audit complete"
```
**Clean-Up Checklist:**
- [ ] No API keys in code
- [ ] No passwords in config files
- [ ] No OAuth tokens committed
- [ ] All secrets use environment variables
- [ ] `.gitignore` includes: `*.env`, `.env.local`, `credentials.json`, `*.pem`, `.vscode/`
---
### Day 3-4: Licensing & Legal ✅
**Tasks:**
- [ ] Add MIT License to root
- [ ] Create CONTRIBUTING.md
- [ ] Create CODE_OF_CONDUCT.md
- [ ] Add license headers to source files
**Files to Create:**
**1. LICENSE** (MIT License)
```
MIT License
Copyright (c) 2025 ProduckAI
[Full MIT license text]
```
**2. CONTRIBUTING.md**
```markdown
# Contributing to ProduckAI
Thank you for your interest in contributing! 🎉
## How to Contribute
1. **Fork the repository**
2. **Create a branch**: `git checkout -b feature/your-feature`
3. **Make changes** and commit: `git commit -m "Add feature"`
4. **Push** to your fork: `git push origin feature/your-feature`
5. **Open a Pull Request**
## Development Setup
```bash
git clone https://github.com/produckai/mcp-server.git
cd mcp-server
python -m venv venv
source venv/bin/activate
pip install -e ".[dev]"
```
## Code Style
- Follow PEP 8
- Use Black for formatting: `black .`
- Use Ruff for linting: `ruff check .`
- Add type hints
- Write docstrings
## Testing
```bash
pytest
pytest --cov
```
## Pull Request Guidelines
- Keep changes focused
- Add tests for new features
- Update documentation
- Follow commit message conventions
## Code of Conduct
Be respectful, inclusive, and professional.
## Questions?
Open an issue or start a discussion!
```
**3. CODE_OF_CONDUCT.md** (Use Contributor Covenant)
---
### Day 5-6: Documentation - Installation & Setup ✅
**Tasks:**
- [ ] Write comprehensive README.md
- [ ] Create INSTALLATION.md
- [ ] Create QUICKSTART.md
- [ ] Document all 50 tools in API_REFERENCE.md
- [ ] Create architecture diagram
**README.md Structure:**
1. Hero section with badges
2. Features list
3. Quick Start (3 steps max)
4. Documentation links
5. Architecture diagram
6. Development setup
7. Contributing guide
8. License
**INSTALLATION.md:**
```markdown
# Installation Guide
## Prerequisites
- Python 3.11 or higher
- pip or conda
- Claude Desktop
- Anthropic API Key
## Step 1: Install via pip
```bash
pip install produckai-mcp-server
```
## Step 2: Configure Environment
Create `~/.produckai/.env`:
```bash
ANTHROPIC_API_KEY=your-key-here
PRODUCKAI_BACKEND_URL=http://localhost:8000
```
## Step 3: Configure Claude Desktop
Edit `~/Library/Application Support/Claude/claude_desktop_config.json`:
```json
{
"mcpServers": {
"produckai": {
"command": "produckai-mcp",
"env": {
"ANTHROPIC_API_KEY": "your-api-key"
}
}
}
}
```
## Step 4: Restart Claude Desktop
## Step 5: Verify Installation
Ask Claude: "List available ProduckAI tools"
You should see 50 tools available.
## Troubleshooting
### Issue: Command not found
**Solution:** Ensure `produckai-mcp` is in PATH
```bash
which produckai-mcp
# Should show path to installed script
```
### Issue: Connection refused
**Solution:** Start backend server first
```bash
# In separate terminal
cd produckai-backend
python -m uvicorn main:app
```
[More troubleshooting...]
```
---
### Day 7: Testing & Demo Data ✅
**Tasks:**
- [ ] Create demo data generator script
- [ ] Generate sample feedback CSV
- [ ] Create demo README
- [ ] Test complete workflow with demo data
- [ ] Record demo video (optional)
**Demo Data Structure:**
```
demo-data/
├── README.md # Instructions
├── feedback.csv # 50 sample feedback items
├── customers.json # Customer metadata
└── quickstart.sh # Automated setup script
```
**Test Checklist:**
- [ ] Install fresh in new environment
- [ ] Upload demo feedback CSV
- [ ] Run clustering
- [ ] Calculate VOC scores
- [ ] Generate PRD
- [ ] Export PRD
- [ ] Verify all 50 tools work
---
## Week 2: Publishing & Launch (Days 8-14)
### Day 8-9: PyPI Publishing Preparation ✅
**Tasks:**
- [ ] Update pyproject.toml with all metadata
- [ ] Create PyPI account
- [ ] Generate API token
- [ ] Test build process locally
- [ ] Publish to Test PyPI first
**pyproject.toml Updates:**
```toml
[project]
name = "produckai-mcp-server"
version = "0.7.0"
description = "AI-powered product feedback analysis and PRD generation for Claude Desktop"
readme = "README.md"
requires-python = ">=3.11"
license = {text = "MIT"}
authors = [
{name = "ProduckAI Team", email = "contact@produckai.com"}
]
keywords = [
"mcp",
"claude",
"product-management",
"feedback-analysis",
"ai",
"prd-generation",
"customer-feedback"
]
classifiers = [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"Intended Audience :: Product Managers",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Topic :: Software Development :: Libraries",
"Topic :: Office/Business",
]
[project.urls]
Homepage = "https://github.com/produckai/mcp-server"
Documentation = "https://github.com/produckai/mcp-server/blob/main/docs"
Repository = "https://github.com/produckai/mcp-server"
Issues = "https://github.com/produckai/mcp-server/issues"
Changelog = "https://github.com/produckai/mcp-server/blob/main/CHANGELOG.md"
```
**Publishing Script:**
```bash
#!/bin/bash
# publish.sh
echo "📦 Building package..."
python -m build
echo "🧪 Testing with Test PyPI..."
python -m twine upload --repository testpypi dist/*
echo "✅ Published to Test PyPI"
echo "Test with: pip install --index-url https://test.pypi.org/simple/ produckai-mcp-server"
read -p "Ready to publish to production PyPI? (y/n) " -n 1 -r
echo
if [[ $REPLY =~ ^[Yy]$ ]]
then
echo "📦 Publishing to PyPI..."
python -m twine upload dist/*
echo "✅ Published to PyPI!"
fi
```
**Commands:**
```bash
# Install build tools
pip install build twine
# Build package
python -m build
# Check package
twine check dist/*
# Upload to Test PyPI
twine upload --repository testpypi dist/*
# Test installation
pip install --index-url https://test.pypi.org/simple/ produckai-mcp-server
# Upload to production PyPI
twine upload dist/*
```
---
### Day 10: GitHub Setup & CI/CD ✅
**Tasks:**
- [ ] Create GitHub repository
- [ ] Push code to GitHub
- [ ] Set up GitHub Actions
- [ ] Configure branch protection
- [ ] Enable Discussions and Issues
**GitHub Actions Workflows:**
**1. `.github/workflows/test.yml`**
```yaml
name: Tests
on:
push:
branches: [ main, develop ]
pull_request:
branches: [ main ]
jobs:
test:
runs-on: ubuntu-latest
strategy:
matrix:
python-version: ['3.11', '3.12']
steps:
- uses: actions/checkout@v3
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v4
with:
python-version: ${{ matrix.python-version }}
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install -e ".[dev]"
- name: Run tests
run: pytest --cov
- name: Run linting
run: |
ruff check .
black --check .
- name: Type checking
run: mypy src/
```
**2. `.github/workflows/publish.yml`**
```yaml
name: Publish to PyPI
on:
release:
types: [published]
jobs:
publish:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: '3.11'
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install build twine
- name: Build package
run: python -m build
- name: Publish to PyPI
env:
TWINE_USERNAME: __token__
TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }}
run: twine upload dist/*
```
**Repository Settings:**
- [ ] Enable branch protection for `main`
- [ ] Require PR reviews (1+)
- [ ] Require status checks (tests pass)
- [ ] Enable Discussions
- [ ] Add topics: `mcp`, `claude`, `product-management`, `ai`
- [ ] Add repository description
- [ ] Set social image (optional)
---
### Day 11: Documentation Polish ✅
**Tasks:**
- [ ] Review all docs for typos
- [ ] Add screenshots/GIFs
- [ ] Create architecture diagram
- [ ] Write CHANGELOG.md
- [ ] Create FAQ.md
**CHANGELOG.md:**
```markdown
# Changelog
All notable changes to this project will be documented in this file.
The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
## [0.7.0] - 2025-11-25
### Added
- PRD generation from insights (6 new tools)
- AI-powered strategic document creation
- Customer segment and persona inference
- Effort-based risk assessment
- Version tracking for PRDs
- Workflow status management (draft/reviewed/approved)
- Markdown export functionality
### Changed
- Updated to support MCP SDK 1.22.0
- Improved error handling across all tools
## [0.6.0] - 2025-11-24
### Added
- JIRA integration (8 tools)
- Enhanced Zoom integration (5 tools)
- VOC scoring engine (4 tools)
- Bidirectional sync between feedback and JIRA
- Auto-fetch Zoom recordings with AI analysis
- 6-dimension VOC prioritization
[... previous versions ...]
## [0.1.0] - 2025-01-01
### Added
- Initial release
- Slack integration
- Basic feedback collection
```
**FAQ.md:**
```markdown
# Frequently Asked Questions
## General
### Q: What is ProduckAI MCP Server?
A: An AI-powered feedback analysis system that integrates with Claude Desktop via MCP protocol. It helps product teams collect, analyze, prioritize, and document customer feedback.
### Q: Is it free?
A: Yes, the software is open source (MIT License). However, you'll need API keys for:
- Anthropic Claude (PRD generation, ~$0.05-0.10 per PRD)
- OpenAI (embeddings, ~$0.01 per 100 items)
### Q: Do I need to run a backend server?
A: For basic usage, the MCP server can work standalone. For advanced features (clustering, insights), you'll need the ProduckAI backend.
## Installation
### Q: Which Python version do I need?
A: Python 3.11 or higher.
### Q: Can I use it without Claude Desktop?
A: The MCP server is designed for Claude Desktop. However, the tools can be called programmatically if needed.
## Usage
### Q: How many feedback items can it handle?
A: Tested with 1,000+ items. Clustering takes ~1-2 minutes for 100 items.
### Q: Can I use my own AI models?
A: Currently supports Anthropic Claude and OpenAI. Custom models can be added via configuration.
### Q: Does it work with on-premise systems?
A: Yes, configure `PRODUCKAI_BACKEND_URL` to point to your internal backend.
[... more FAQs ...]
```
---
### Day 12: Community Setup ✅
**Tasks:**
- [ ] Create GitHub Discussions categories
- [ ] Write first discussion post (introduction)
- [ ] Set up issue templates
- [ ] Create label system
- [ ] Write SECURITY.md
**GitHub Issue Templates:**
**Bug Report (`.github/ISSUE_TEMPLATE/bug_report.md`):**
```markdown
---
name: Bug report
about: Create a report to help us improve
title: '[BUG] '
labels: bug
assignees: ''
---
**Describe the bug**
A clear description of what the bug is.
**To Reproduce**
Steps to reproduce:
1. ...
2. ...
**Expected behavior**
What you expected to happen.
**Screenshots**
If applicable, add screenshots.
**Environment:**
- OS: [e.g., macOS 13]
- Python version: [e.g., 3.11]
- MCP Server version: [e.g., 0.7.0]
**Additional context**
Any other context about the problem.
```
**Feature Request (`.github/ISSUE_TEMPLATE/feature_request.md`):**
```markdown
---
name: Feature request
about: Suggest an idea
title: '[FEATURE] '
labels: enhancement
assignees: ''
---
**Is your feature request related to a problem?**
A clear description of the problem.
**Describe the solution you'd like**
A clear description of what you want to happen.
**Describe alternatives you've considered**
Alternative solutions or features you've considered.
**Additional context**
Add any other context or screenshots.
```
**GitHub Labels:**
```
Type:
- bug
- enhancement
- documentation
- question
Priority:
- priority: high
- priority: medium
- priority: low
Status:
- status: investigating
- status: in progress
- status: needs review
```
**SECURITY.md:**
```markdown
# Security Policy
## Supported Versions
| Version | Supported |
| ------- | ------------------ |
| 0.7.x | :white_check_mark: |
| 0.6.x | :white_check_mark: |
| < 0.6 | :x: |
## Reporting a Vulnerability
**Do not report security vulnerabilities through public GitHub issues.**
Instead, please report them via email to: security@produckai.com
You should receive a response within 48 hours.
Please include:
- Type of issue (e.g., SQL injection, XSS)
- Full paths of source file(s) related to the issue
- Location of the affected source code
- Step-by-step instructions to reproduce
- Proof-of-concept or exploit code (if possible)
- Impact of the issue
## Disclosure Policy
We follow coordinated disclosure:
1. Security issue reported
2. Issue confirmed and fix developed
3. Fix released
4. Public disclosure after 90 days (or sooner if mutually agreed)
```
---
### Day 13: Marketing & Announcement ✅
**Tasks:**
- [ ] Write announcement blog post
- [ ] Create Twitter thread
- [ ] Post on Reddit (r/programming, r/ProductManagement)
- [ ] Post on Hacker News
- [ ] Share on LinkedIn
- [ ] Submit to Product Hunt (optional)
**Announcement Template:**
**Blog Post / GitHub Discussion:**
```markdown
# Introducing ProduckAI MCP Server: AI-Powered Feedback Analysis for Claude Desktop
We're excited to announce the open source release of **ProduckAI MCP Server**, a comprehensive feedback analysis system that integrates with Claude Desktop.
## What is it?
ProduckAI transforms scattered customer feedback into actionable product requirements documents (PRDs) using AI-powered analysis.
## Key Features
- **Multi-Source Collection**: Slack, Google Drive, Zoom, JIRA, CSV
- **AI-Powered Analysis**: Automatic clustering and insight generation
- **Smart Prioritization**: 6-dimension VOC scoring
- **PRD Generation**: Strategic documents from customer feedback
- **50 Tools**: Complete workflow from collection to execution
## Why We Built It
Product teams spend hours manually:
- Collecting feedback from various sources
- Categorizing and prioritizing issues
- Writing PRDs with evidence
ProduckAI automates this entire workflow, reducing PRD creation time by 70% while ensuring every decision is backed by customer evidence.
## Try It Today
```bash
pip install produckai-mcp-server
```
See [Getting Started Guide](link) for setup instructions.
## What's Next
We're actively developing:
- Integration with Linear, Asana, Monday
- Custom PRD templates
- Real-time feedback dashboards
- Churn prediction models
## Get Involved
- ⭐ Star on [GitHub](https://github.com/produckai/mcp-server)
- 📖 Read the [documentation](link)
- 💬 Join [discussions](link)
- 🐛 Report [issues](link)
Thank you to the open source community! 🙏
```
**Twitter Thread:**
```
🚀 Excited to announce ProduckAI MCP Server is now open source!
Transform customer feedback → Strategic PRDs using AI
🔗 github.com/produckai/mcp-server
Features:
✅ 50 tools for complete feedback workflow
✅ Multi-source ingestion (Slack, Zoom, JIRA)
✅ AI clustering & insights
✅ VOC scoring
✅ PRD generation with Claude
1/5
Why it matters:
Product teams waste hours collecting feedback, categorizing issues, and writing PRDs.
ProduckAI automates this entire workflow:
- Sync from all sources
- AI-powered analysis
- Smart prioritization
- Generate strategic PRDs in 10 seconds
2/5
Built with:
- MCP Protocol for Claude Desktop
- Claude Sonnet 4.5 for PRD generation
- 50 specialized tools
- Comprehensive workflow support
Open source (MIT License) 🎉
3/5
Try it today:
pip install produckai-mcp-server
See docs: [link]
Join the community:
⭐ Star on GitHub
💬 Discussions
🐛 Issues
4/5
What's next:
- Custom PRD templates
- Linear/Asana integration
- Real-time dashboards
- Churn prediction
Contributions welcome! See CONTRIBUTING.md
5/5
```
---
### Day 14: Launch & Monitor ✅
**Launch Checklist:**
- [ ] Publish to PyPI (production)
- [ ] Push to GitHub (public)
- [ ] Post announcements (Twitter, Reddit, HN)
- [ ] Monitor GitHub issues
- [ ] Respond to questions in Discussions
- [ ] Track PyPI download stats
- [ ] Update website (if applicable)
**Post-Launch Monitoring:**
**Week 1 Metrics to Track:**
- GitHub stars
- PyPI downloads
- Issues opened
- Pull requests
- Discussion engagement
**Week 2-4:**
- Identify common issues
- Prioritize bug fixes
- Plan next features based on feedback
- Release patch versions as needed
---
## Testing Guide for New Users
### Quick Test Script
Create `test-install.sh` for users to validate installation:
```bash
#!/bin/bash
# test-install.sh - Quick validation script
echo "🧪 Testing ProduckAI MCP Server Installation"
echo "=" `echo "="*50`
# Check Python version
echo "✓ Checking Python version..."
python_version=$(python --version 2>&1 | awk '{print $2}')
echo " Python $python_version"
# Check if package installed
echo "✓ Checking package installation..."
pip show produckai-mcp-server > /dev/null 2>&1
if [ $? -eq 0 ]; then
version=$(pip show produckai-mcp-server | grep Version | awk '{print $2}')
echo " ProduckAI MCP Server $version installed"
else
echo " ❌ Package not found. Run: pip install produckai-mcp-server"
exit 1
fi
# Check command availability
echo "✓ Checking command availability..."
which produckai-mcp > /dev/null 2>&1
if [ $? -eq 0 ]; then
echo " produckai-mcp command found"
else
echo " ⚠️ produckai-mcp command not in PATH"
fi
# Check environment variables
echo "✓ Checking environment variables..."
if [ -z "$ANTHROPIC_API_KEY" ]; then
echo " ⚠️ ANTHROPIC_API_KEY not set"
else
echo " ANTHROPIC_API_KEY configured"
fi
# Check Claude Desktop config
echo "✓ Checking Claude Desktop configuration..."
claude_config="$HOME/Library/Application Support/Claude/claude_desktop_config.json"
if [ -f "$claude_config" ]; then
if grep -q "produckai" "$claude_config"; then
echo " ProduckAI configured in Claude Desktop"
else
echo " ⚠️ ProduckAI not found in Claude Desktop config"
echo " See docs/INSTALLATION.md for setup instructions"
fi
else
echo " ℹ️ Claude Desktop config not found"
fi
echo "=" `echo "="*50`
echo "✅ Installation check complete"
echo ""
echo "Next steps:"
echo "1. Set ANTHROPIC_API_KEY environment variable"
echo "2. Configure Claude Desktop (see docs/INSTALLATION.md)"
echo "3. Restart Claude Desktop"
echo "4. Try: 'List available ProduckAI tools' in Claude"
```
---
## Maintenance Plan
### Version Release Schedule
- **Patch releases (0.7.x):** As needed for bugs
- **Minor releases (0.x.0):** Monthly with new features
- **Major releases (x.0.0):** Quarterly with breaking changes
### Support Commitments
- **Security issues:** 48-hour response time
- **Critical bugs:** 1-week fix
- **Feature requests:** Reviewed monthly
- **Documentation:** Updated with each release
### Community Guidelines
- **Be responsive:** Reply to issues within 2-3 days
- **Be welcoming:** Encourage new contributors
- **Be transparent:** Share roadmap and decisions
- **Be consistent:** Follow conventions, maintain code quality
---
## Success Metrics (First Month)
**Target Goals:**
- [ ] 100+ GitHub stars
- [ ] 500+ PyPI downloads
- [ ] 10+ community contributors
- [ ] 5+ feature contributions
- [ ] 0 critical security issues
**Nice to Have:**
- [ ] Featured on MCP community showcase
- [ ] Blog post from Anthropic
- [ ] Product Hunt top 10
- [ ] Mentions in product management newsletters
---
## Checklist Summary
### Pre-Launch
- [ ] Security audit complete
- [ ] All secrets removed
- [ ] MIT License added
- [ ] Documentation complete (README, INSTALLATION, CONTRIBUTING)
- [ ] Demo data generated
- [ ] Tests pass
- [ ] PyPI package builds successfully
### Launch Day
- [ ] Published to PyPI
- [ ] Pushed to GitHub (public)
- [ ] Announcements posted (Twitter, Reddit, HN)
- [ ] GitHub Discussions enabled
- [ ] Issue templates configured
### Post-Launch (Week 1)
- [ ] Monitor issues daily
- [ ] Respond to questions
- [ ] Fix critical bugs
- [ ] Update docs based on feedback
---
**Status:** Ready to begin Week 1 🚀
