# AWS Sage MCP - Marketing & Visibility Strategy
## Executive Summary
This document outlines a comprehensive marketing strategy to maximize visibility and adoption of AWS Sage MCP - a production-grade Model Context Protocol server for AWS that unifies 30 intelligent tools in one server.
## Completed Actions ✅
### 1. Repository Optimization
- **Renamed**: `aws-sage` → `aws-sage-mcp` (follows 40% industry naming pattern)
- **Updated Description**: Production-grade MCP server for AWS - 30 intelligent tools, cross-service discovery, dependency mapping, cost analysis
- **Added Topics**: mcp, mcp-server, aws, python, claude, ai, devops, localstack, cost-optimization
## Immediate Next Steps (Priority Order)
### Phase 1: Package Registry Presence (Week 1)
#### 1.1 Publish to PyPI ⚡ CRITICAL
**Action Items:**
```bash
# Update pyproject.toml
name = "aws-sage-mcp"
version = "1.0.4"
description = "Production-grade MCP server for AWS"
keywords = ["mcp", "aws", "model-context-protocol", "claude", "ai-agents"]
# Add README metadata for MCP Registry validation
# At top of README.md add:
# mcp-name: io.github.arunsanna/aws-sage-mcp
# Publish
python -m build
twine upload dist/*
```
**Why**: PyPI listing is REQUIRED for MCP Registry submission
**Timeline**: Immediate (today)
#### 1.2 Submit to MCP Registry ⚡ CRITICAL
**Requirements Met:**
✅ Python package ready
✅ MIT License
✅ README with documentation
✅ Active development
**Steps:**
1. Create `server.json` in repo root:
```json
{
"$schema": "https://static.modelcontextprotocol.io/schemas/2025-07-09/server.schema.json",
"name": "io.github.arunsanna/aws-sage-mcp",
"description": "Production-grade MCP server for AWS - 30 intelligent tools, cross-service discovery, dependency mapping, cost analysis, and LocalStack support",
"version": "1.0.4",
"packages": [
{
"registry_type": "pypi",
"identifier": "aws-sage-mcp",
"version": "1.0.4"
}
],
"homepage": "https://github.com/arunsanna/aws-sage-mcp",
"license": "MIT",
"repository": {
"type": "git",
"url": "https://github.com/arunsanna/aws-sage-mcp"
}
}
```
2. Authenticate with GitHub:
```bash
# Install mcp-publisher CLI
go install github.com/modelcontextprotocol/registry/cmd/mcp-publisher@latest
# Login via GitHub OAuth
mcp-publisher login github
# Publish
mcp-publisher publish
```
**Expected Result**: Listed at https://registry.modelcontextprotocol.io within 24 hours
**Timeline**: Day 2
### Phase 2: Community Engagement (Week 1-2)
#### 2.1 Reddit & Forum Outreach
**Target Communities:**
- r/aws (550k members)
- r/artificial (2.8M members)
- r/LocalLLaMA (200k+ members)
- r/ChatGPT (6M members)
- r/ClaudeAI (50k+ members)
**Post Strategy:**
**Title**: "I built AWS Sage MCP - One unified MCP server that replaces AWS Labs' 15 separate servers"
**Content Format:**
- Problem: AWS Labs has 15 fragmented MCP servers
- Solution: AWS Sage - 1 server, 30 tools, cross-service intelligence
- Unique features: Cost analysis, LocalStack, dependency mapping
- Link to GitHub + MCP Registry
- Ask for feedback
**Timeline**: Day 3-5
**Expected**: 50-200 upvotes, 1000+ views per post
#### 2.2 Discord Communities
**Target Servers:**
- MCP Official Discord (#showcase, #registry-dev)
- Anthropic Discord
- AWS Community Discord
- LocalStack Discord
**Approach:**
- Share in #showcase channels
- Offer to help users migrate from AWS Labs servers
- Provide LocalStack integration examples
**Timeline**: Day 3-7
#### 2.3 Twitter/X Campaign
**Strategy:**
- Thread announcing launch
- Tag @AnthropicAI, @awscloud, @LocalStack
- Use hashtags: #MCP #ModelContextProtocol #AWS #AI #CloudComputing
- Post comparison table: AWS Labs vs AWS Sage
- Share LocalStack use case
**Content Calendar:**
- Day 1: Launch announcement
- Day 3: Comparison table
- Day 5: Cost optimization demo
- Day 7: LocalStack tutorial
**Timeline**: Week 1
### Phase 3: Content Marketing (Week 2-4)
#### 3.1 Blog Posts
**Platforms:**
- Dev.to (high SEO value)
- Medium
- Hashnode
- Personal blog
**Article Ideas:**
1. "Why I Built AWS Sage: Unifying AWS MCP Servers" (launch story)
2. "AWS Cost Optimization with AI Agents: A Practical Guide" (SEO: "aws cost optimization ai")
3. "Testing AWS Infrastructure Locally with MCP and LocalStack" (SEO: "localstack mcp")
4. "From 15 Servers to 1: Simplifying AWS MCP Integration" (comparison)
**Timeline**: 1 post/week for 4 weeks
**Expected**: 2000-5000 views per article
#### 3.2 YouTube Content
**Video Ideas:**
1. "5-Minute Setup: AWS Sage MCP in Claude Desktop" (tutorial)
2. "Find AWS Cost Savings with AI Agents" (demo)
3. "LocalStack + MCP: Test AWS Without Spending Money" (tutorial)
4. "AWS Sage vs AWS Labs MCP: Which Should You Use?" (comparison)
**Format:**
- 5-10 minute videos
- Screen recordings with voiceover
- Clear CTAs to GitHub
**Timeline**: 1 video/week for 4 weeks
**Expected**: 500-2000 views per video
### Phase 4: Strategic Partnerships (Week 3-8)
#### 4.1 Anthropic Partnership
**Approach:**
- Email developer relations team
- Highlight unique features (cost analysis, LocalStack)
- Offer to create official tutorial
- Request feature in Claude blog/newsletter
**Contact:** devrel@anthropic.com
**Timeline**: Week 3
#### 4.2 AWS Community
**Approach:**
- Submit to AWS Community Builders program
- Contact AWS DevRel team
- Share on AWS subreddits
- Mention in AWS user groups
**Timeline**: Week 3-4
#### 4.3 LocalStack Partnership
**Approach:**
- Contact LocalStack team about MCP integration
- Offer joint blog post/webinar
- Request feature on LocalStack blog
- Add to LocalStack integrations page
**Contact**: Via GitHub or their Slack
**Timeline**: Week 4
### Phase 5: SEO & Documentation (Ongoing)
#### 5.1 SEO Optimization
**Target Keywords:**
- "aws mcp server" (low competition)
- "model context protocol aws" (low competition)
- "aws cost optimization mcp" (very low competition)
- "localstack mcp integration" (very low competition)
- "claude aws integration" (medium competition)
**Actions:**
- Update README with keyword-rich H2/H3 headers
- Create dedicated landing page (GitHub Pages)
- Add structured data (JSON-LD)
- Build backlinks from blog posts
**Timeline**: Week 2 onwards
#### 5.2 Documentation Enhancement
**Add:**
- Video tutorials (embedded)
- Interactive examples
- Comparison table with AWS Labs
- Migration guide from AWS Labs servers
- LocalStack quick-start guide
- Cost optimization case studies
**Timeline**: Ongoing
### Phase 6: Community Building (Week 4 onwards)
#### 6.1 GitHub Community
**Actions:**
- Enable GitHub Discussions
- Create issue templates
- Set up GitHub Sponsors
- Add CONTRIBUTING.md guidelines
- Create project roadmap
- Weekly issue triage
**Timeline**: Week 4
#### 6.2 Newsletter
**Strategy:**
- Monthly newsletter (via Substack/Beehiiv)
- Share AWS + MCP tips
- Feature community contributions
- Announce new features
- Share cost optimization wins
**Timeline**: Month 2
## Success Metrics
### Month 1 Targets:
- ⭐ GitHub Stars: 50+ (currently 5)
- 📦 PyPI Downloads: 500+
- 👥 MCP Registry Installs: 100+
- 📝 Blog Post Views: 10,000+
- 🎥 YouTube Views: 2,000+
- 💬 Discord/Reddit Engagement: 50+ conversations
### Month 3 Targets:
- ⭐ GitHub Stars: 200+
- 📦 PyPI Downloads: 2,000+
- 👥 MCP Registry Installs: 500+
- 📝 Total Content Views: 50,000+
- 🤝 Partner Features: 2+
- 💬 Active Community: 20+ contributors
### Month 6 Targets:
- ⭐ GitHub Stars: 500+
- 📦 PyPI Downloads: 10,000+
- 👥 MCP Registry Installs: 2,000+
- 📝 Total Content Views: 200,000+
- 🤝 Official AWS/Anthropic mention
- 💬 Active Community: 50+ contributors
## Competitive Advantages to Emphasize
### 1. **Unified Architecture**
- AWS Labs: 15 servers, 45 tools, complex setup
- AWS Sage: 1 server, 30 tools, 5-minute setup
- **Message**: "Simplicity without sacrifice"
### 2. **Unique Features**
- Cross-service resource discovery
- Dependency mapping & impact analysis
- Cost optimization analyzer
- LocalStack integration
- **Message**: "Intelligence, not just integration"
### 3. **Production-Ready**
- 3-tier safety system
- 70+ blocked dangerous operations
- Multi-account support
- Docker containerization
- **Message**: "Enterprise-grade, open-source"
### 4. **Developer Experience**
- Works with ALL MCP clients
- Comprehensive documentation
- LocalStack for free testing
- Active support
- **Message**: "Built by developers, for developers"
## Budget Considerations
### Free Channels (Maximum Impact):
- ✅ GitHub (primary hub)
- ✅ Reddit (massive reach)
- ✅ Discord communities
- ✅ Twitter/X (free tier)
- ✅ Dev.to, Medium, Hashnode
- ✅ YouTube (free hosting)
- ✅ Email outreach
### Optional Paid (Low Budget):
- AWS Community Builders (application-based, free)
- GitHub Sponsors (for sustainability)
- Promoted tweets ($50-100/month)
- Reddit ads (if budget allows)
**Total Est. Budget**: $0-200/month
## Action Plan - This Week
### Monday:
- [ ] Update pyproject.toml with new name
- [ ] Add mcp-name to README
- [ ] Build and publish to PyPI
- [ ] Create server.json
### Tuesday:
- [ ] Install mcp-publisher
- [ ] Authenticate with GitHub
- [ ] Publish to MCP Registry
- [ ] Verify listing
### Wednesday:
- [ ] Write Reddit launch post
- [ ] Post to r/aws, r/artificial
- [ ] Share in MCP Discord
- [ ] Tweet launch thread
### Thursday:
- [ ] Engage with Reddit comments
- [ ] Answer Discord questions
- [ ] Start first blog post
- [ ] Plan YouTube video
### Friday:
- [ ] Monitor analytics
- [ ] Document lessons learned
- [ ] Plan next week content
- [ ] Engage with early adopters
## Conclusion
AWS Sage MCP has significant potential to become the go-to MCP server for AWS. The key differentiators (unified architecture, cost analysis, LocalStack support) address real pain points. With consistent execution of this strategy, we can achieve:
1. **Immediate**: PyPI + MCP Registry presence (Week 1)
2. **Short-term**: Community awareness & adoption (Month 1)
3. **Medium-term**: Thought leadership & partnerships (Month 3)
4. **Long-term**: Ecosystem standard for AWS MCP integration (Month 6+)
The focus should be on:
- **Quality over quantity**: Better to have 100 engaged users than 1000 passive stars
- **Community first**: Help users succeed, adoption follows naturally
- **Consistent execution**: Weekly content, daily engagement
- **Leverage uniqueness**: Don't compete on features AWS Labs has - compete on intelligence and simplicity
Let's make AWS Sage MCP the default choice for AWS + AI agent workflows! 🚀
