# Cite-Finance-MCP: Execution Masterplan to $10k/mo
**Product:** Python MCP Server for financial data with citations
**Status:** Live on PyPI v0.1.0, Gumroad logic added (not pushed)
**Competitor:** Financial Datasets MCP (735 stars)
**Constraints:** No Ads, No Community Building, No Hosting Costs
**Goal:** $10,000/mo in 90 days
---
## 🎯 Strategic Positioning: "Zero Hallucination Financial Data"
### Core Value Proposition
> "The only MCP server that **proves** every number with SEC citations. Built for Claude Desktop users who refuse to trust AI guesses with their money."
### Competitive Advantage vs Financial Datasets MCP
| Factor | Financial Datasets MCP | Cite-Finance-MCP | Winner |
|--------|----------------------|------------------|---------|
| **Data Coverage** | 735 stars, broad | Focused (SEC + citations) | Them (breadth) |
| **Hallucination Risk** | Unknown/High | Zero (provenance) | **Us** |
| **Trust Layer** | Missing | SEC URLs + consistency score | **Us** |
| **MCP Native** | Yes | Yes | Tie |
| **Monetization** | Unknown | Gumroad (serverless) | **Us** |
**Wedge Strategy:** Position as "Premium Alternative" - they're Walmart, we're Whole Foods.
---
## 🏗️ Technical Architecture: Serverless Monetization
### Problem: No Backend, No Hosting Budget
**Solution:** Hybrid Static + Spectator tunnel architecture
### Architecture: 3-Tier Data Serving
```
┌─────────────────────────────────────────────────────────────┐
│ TIER 1: Demo Data (FREE - Built into MCP Package) │
├─────────────────────────────────────────────────────────────┤
│ • Static JSON embedded in mcp_server.py │
│ • 5-10 tickers (AAPL, MSFT, NVDA, TSLA, GOOGL) │
│ • Last 2 years of quarterly data │
│ • Purpose: Hook users, showcase citations │
│ • Cost: $0 (bundled in PyPI package) │
└─────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────┐
│ TIER 2: Extended Cache (PAID - Gumroad $29 one-time) │
├─────────────────────────────────────────────────────────────┤
│ • Static parquet file (~50MB) downloaded post-purchase │
│ • 500 tickers x 5 years of data │
│ • Delivered via Gumroad file delivery │
│ • User stores locally, MCP reads from disk │
│ • Update: New file every quarter ($9 update fee) │
│ • Cost: $0 hosting (user-side storage) │
└─────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────┐
│ TIER 3: Live API (PAID - Gumroad $99/mo subscription) │
├─────────────────────────────────────────────────────────────┤
│ • SSH tunnel to Spectator node (100.96.62.97) │
│ • Real-time SEC scraping on-demand │
│ • Unlimited tickers, latest filings │
│ • User provides SSH key, we provision Spectator access │
│ • Cost: $0 (Spectator already running 24/7) │
└─────────────────────────────────────────────────────────────┘
```
### Implementation Details
#### Tier 1: Demo Data (Already Built)
```python
# mcp_server.py (current)
DEMO_DATA = {
"AAPL": {"revenue_ttm": 383285000000, "source": "SEC 10-K", "url": "..."},
"TSLA": {"revenue_ttm": 96770000000, "source": "SEC 10-K", "url": "..."}
}
```
**Enhancement:** Expand to 10 tickers, add more metrics (net_income, cash, debt).
#### Tier 2: Extended Static Cache (NEW)
```python
# mcp_server.py (add)
CACHE_PATH = os.path.expanduser("~/.cite-finance/financial_data.parquet")
def load_cache():
if os.path.exists(CACHE_PATH):
return pd.read_parquet(CACHE_PATH)
return None
# On first paid user request
if not API_KEY and not os.path.exists(CACHE_PATH):
return "Buy Extended Cache: https://gumroad.com/l/cite-finance-cache"
```
**Delivery Workflow:**
1. User buys on Gumroad ($29)
2. Gumroad emails download link (financial_data.parquet)
3. User downloads, places in `~/.cite-finance/`
4. MCP auto-detects and uses local cache
5. Zero server cost
#### Tier 3: Live Spectator Tunnel (NEW)
```python
# mcp_server.py (add)
SPECTATOR_ENABLED = os.getenv("CITE_SPECTATOR_KEY") is not None
async def fetch_from_spectator(ticker: str, metric: str) -> dict:
"""SSH into Spectator, scrape SEC on-demand."""
ssh_key = os.getenv("CITE_SPECTATOR_KEY")
# SSH to spectator
result = subprocess.run([
"ssh", "-i", ssh_key,
"spectator@100.96.62.97",
f"python3 /home/spectator/scrapers/sec_query.py {ticker} {metric}"
], capture_output=True, timeout=30)
return json.loads(result.stdout)
```
**Provisioning Workflow:**
1. User buys $99/mo subscription on Gumroad
2. Gumroad webhook → Lambda → Generate SSH key for user
3. Email user: "Set CITE_SPECTATOR_KEY=/path/to/key.pem"
4. MCP detects key, enables live mode
5. Cost: $0 (Spectator capacity already exists)
---
## 💰 Monetization: Gumroad-Only (Serverless)
### Why Gumroad (not Stripe/Paddle)
- ✅ No webhook server needed (email-based delivery)
- ✅ Handles VAT/sales tax automatically
- ✅ File delivery built-in (for Tier 2 cache)
- ✅ Subscription support (for Tier 3)
- ✅ 10% fee (vs Stripe 2.9% + $0.30 + webhook infra)
### Pricing Strategy
| Tier | Price | Value | Target |
|------|-------|-------|--------|
| **Demo** | Free | 10 tickers, 2 years | Hook users |
| **Extended Cache** | $29 one-time | 500 tickers, 5 years | Indie devs |
| **Cache Updates** | $9/quarter | Latest filings | Retention |
| **Live API** | $99/mo | Unlimited, real-time | Pro users |
| **Enterprise** | $499/mo | White-label + support | Companies |
### Revenue Model Math
**Conservative (90 Days):**
```
Month 1: 1000 installs → 20 cache ($580) + 2 live ($198) = $778
Month 2: 2000 installs → 40 cache ($1160) + 5 live ($495) = $1655
Month 3: 3500 installs → 70 cache ($2030) + 10 live ($990) = $3020
Total Revenue: $5,453 (54% of goal)
```
**Aggressive (90 Days):**
```
Month 1: 2000 installs → 40 cache ($1160) + 4 live ($396) = $1556
Month 2: 5000 installs → 100 cache ($2900) + 12 live ($1188) = $4088
Month 3: 10000 installs → 200 cache ($5800) + 25 live ($2475) = $8275
Total Revenue: $13,919 (139% of goal) ✅
```
**Key Assumption:** 2% conversion to paid (industry standard for dev tools).
---
## 🚀 Traffic Strategy: Zero Community Building
### Constraint Interpretation
"No community building" = No Discord, No Twitter replies, No support forums.
**Allowed:** SEO, Documentation, Registry optimization, One-way content.
### Traffic Sources (Ranked by ROI)
#### 1. Smithery Registry Optimization (Week 1)
**Current:** Basic listing
**Goal:** Feature placement
**Actions:**
- [ ] Enhance `smithery.yaml` with rich metadata
- [ ] Add tags: `finance`, `sec`, `compliance`, `zero-hallucination`
- [ ] Request "Featured" badge (email Smithery team)
- [ ] Add demo GIF showing SEC citation flow
**Expected Impact:** 500-1000 installs/month from featured placement.
#### 2. PyPI SEO (Week 1-2)
**Current:** v0.1.0 published, minimal README
**Goal:** First result for "mcp financial data"
**Actions:**
- [ ] Expand PyPI README to 2000+ words
- [ ] Add keywords: `mcp-server`, `financial-data`, `sec-edgar`, `citations`
- [ ] Include code examples with output screenshots
- [ ] Add "Alternatives" section comparing to Financial Datasets MCP
- [ ] Link to GitHub repo (creates backlink for SEO)
**Expected Impact:** 200-500 organic installs/month.
#### 3. MCP Official Directory (Week 2)
**Repo:** https://github.com/modelcontextprotocol/servers
**Actions:**
- [ ] Submit PR to add cite-finance-mcp to directory
- [ ] Follow their template (name, description, install command)
- [ ] Highlight unique feature: "Only MCP with SEC provenance"
**Expected Impact:** 1000-2000 installs from official endorsement.
#### 4. Documentation SEO (Week 2-3)
**Create:** `docs.cite-finance.io` (GitHub Pages, free)
**Target Keywords:**
- "claude desktop financial data"
- "mcp server sec filings"
- "zero hallucination financial api"
- "ai financial assistant without errors"
**Content:**
- Installation guide
- Usage examples (with Claude Desktop screenshots)
- "Why Citations Matter" (thought leadership)
- Comparison table (vs Financial Datasets MCP)
**Expected Impact:** 500-1000 installs/month from Google.
#### 5. "Show HN" Launch (Week 3)
**Title:** "Show HN: MCP Server for Financial Data with SEC Citations"
**Body:**
```
I built cite-finance-mcp because I was tired of Claude making up
financial numbers. Every figure includes a direct link to the
SEC filing. Zero hallucination.
Try it free (10 tickers), upgrade to 500 tickers for $29.
Feedback welcome on the citation UX - still refining how to
surface provenance without cluttering the response.
```
**Expected Impact:** 2000-5000 installs in 48 hours.
#### 6. Reddit (Strategic Posts, Not Community)
**Subreddits:** r/ClaudeAI, r/LocalLLaMA, r/MachineLearning
**Post Type:** "I made a thing" (not engagement farming)
**Example Title (r/ClaudeAI):**
"MCP Server that cites every financial number (SEC URLs included)"
**Expected Impact:** 500-1000 installs per successful post.
---
## 📊 Traffic Funnel Metrics
### Installation Journey
```
1000 Registry Views
↓ (30% CTR)
300 PyPI Page Views
↓ (20% install rate)
60 Installations
↓ (50% activation)
30 Active Users
↓ (2% conversion)
1 Paid Customer ($29 or $99)
```
**To hit $10k/mo:**
- Need ~100 paid customers (mix of $29 and $99)
- Need ~5,000 active users
- Need ~10,000 installations
- Need ~50,000 registry views
**Timeline:** 90 days = Aggressive but achievable with featured placement.
---
## 🛠️ Technical Roadmap
### Week 1: Foundation
- [ ] Expand demo data to 10 tickers (5 → 10)
- [ ] Add 5 more metrics (revenue, net_income, cash, debt, equity)
- [ ] Create `financial_data.parquet` (500 tickers, 5 years)
- [ ] Test local cache loading in MCP
- [ ] Update `smithery.yaml` with rich metadata
- [ ] Enhance PyPI README (2000+ words)
### Week 2: Monetization
- [ ] Create Gumroad products:
- [ ] Extended Cache ($29 one-time)
- [ ] Cache Updates ($9/quarter subscription)
- [ ] Live API ($99/mo subscription)
- [ ] Test Gumroad file delivery workflow
- [ ] Write provisioning script for Spectator SSH keys
- [ ] Add license key validation to `mcp_server.py`
- [ ] Create "Upgrade" prompts in demo mode responses
### Week 3: Distribution
- [ ] Submit to MCP official directory (PR)
- [ ] Launch GitHub Pages docs site
- [ ] Write 5 SEO-optimized documentation pages
- [ ] Post "Show HN" on Hacker News
- [ ] Post to r/ClaudeAI
- [ ] Email Smithery for featured placement
### Week 4-8: Iteration
- [ ] Monitor conversion funnel (installs → paid)
- [ ] Add most-requested metrics (P/E, EPS, margins)
- [ ] Optimize cache file size (compression)
- [ ] Add SQLite option (alternative to parquet)
- [ ] Create video demo (Loom, 2 min)
- [ ] Add testimonials from early users
### Week 9-12: Scale
- [ ] Launch Enterprise tier ($499/mo)
- [ ] Add Paddle as payment option (lower fees at scale)
- [ ] Create affiliate program (20% commission)
- [ ] Optimize for "claude desktop plugins" keyword
- [ ] Expand demo data to 20 tickers
- [ ] Add caching layer for Spectator queries (Redis)
---
## 🏁 Success Metrics
### Leading Indicators (Weekly)
- **Installs:** 100 → 500 → 1000 → 2000/week
- **Activation Rate:** >50% (user runs first query)
- **Demo → Paid CTR:** >2%
- **PyPI Daily Downloads:** 50 → 200 → 500/day
### Lagging Indicators (Monthly)
- **MRR:** $500 → $2000 → $5000 → $10,000
- **Paid Users:** 5 → 20 → 50 → 100
- **Churn Rate:** <5% monthly
- **Spectator Uptime:** >99%
### Red Flags (Act Immediately)
- ⚠️ Activation rate <30% (UX problem)
- ⚠️ Conversion rate <1% (pricing problem)
- ⚠️ Churn rate >10% (quality problem)
- ⚠️ Spectator downtime >1% (reliability problem)
---
## 🎯 Competitive Response Plan
### If Financial Datasets MCP Adds Citations
**Response:** Position as "Compliance-First" alternative.
- Emphasize: SEC-specific, regulatory-grade provenance
- Add: Audit trail export (for compliance teams)
- Target: Financial institutions, not indie devs
### If They Launch Paid Tier
**Response:** Undercut on price (they'll go SaaS, we're static).
- Our $29 = lifetime access to 500 tickers
- Their $X/mo = recurring cost
- Marketing: "Own your data, don't rent it"
### If They Copy Our Model
**Response:** We're 3 months ahead, focus on execution.
- By then: 10k installs, 100 paid customers, SEO dominance
- Leverage: First-mover advantage in MCP ecosystem
---
## 💡 Contingency Plans
### Plan B: If Spectator Dies
**Fallback:** Use free SEC API (sec-api.io free tier).
- Tier 3 becomes "Premium SEC API" (we're just wrapper)
- Cost: $0 (within free tier limits)
- Trade-off: Slower, less flexible
### Plan C: If Gumroad Sucks
**Migrate to:** Paddle (lower fees, better automation).
- Wait until $5k/mo to justify migration effort
- Use Paddle's webhook → CloudFlare Worker (serverless)
### Plan D: If MCP Ecosystem Fails
**Pivot:** Convert to Obsidian plugin (similar architecture).
- Market: Knowledge workers, researchers
- Same value prop: Zero hallucination financial notes
- Estimated market: 100k+ Obsidian users interested in finance
---
## 🚨 Critical Blockers
### 1. PyPI Package Broken?
**Test:** `pip install cite-finance-mcp` in fresh env
**Fix:** If broken, republish with `hatch build && twine upload dist/*`
### 2. Spectator SSH Access
**Verify:** Can we SSH to 100.96.62.97 from external client?
**Fix:** If no, use Tailscale tunnel (user installs Tailscale)
### 3. Demo Data Stale
**Check:** Are AAPL/TSLA numbers still current?
**Fix:** Update with Q4 2024 or Q1 2025 data
---
## 📈 Revenue Milestones
### $1k/mo (Month 1)
- 5,000 total installs
- 30 Extended Cache sales ($870)
- 2 Live API subs ($198)
- **Unlock:** Validate product-market fit
### $5k/mo (Month 2)
- 15,000 total installs
- 100 cache sales ($2,900)
- 20 Live API subs ($1,980)
- **Unlock:** Quit side projects, focus here
### $10k/mo (Month 3)
- 30,000 total installs
- 200 cache sales ($5,800)
- 40 Live API subs ($3,960)
- 2 Enterprise deals ($998)
- **Unlock:** Sustainable income, consider full-time
---
## ✅ Immediate Action Plan (Next 7 Days)
### Day 1 (Today)
- [ ] Test current PyPI package (install + run)
- [ ] Audit demo data (ensure accuracy)
- [ ] Create Gumroad account (if not exists)
### Day 2
- [ ] Generate `financial_data.parquet` (500 tickers)
- [ ] Test local cache loading logic
- [ ] Write Extended Cache product page on Gumroad
### Day 3
- [ ] Enhance `smithery.yaml` (rich metadata)
- [ ] Rewrite PyPI README (2000+ words)
- [ ] Create demo GIF (30 sec, shows citation)
### Day 4
- [ ] Submit to MCP official directory (PR)
- [ ] Email Smithery for featured placement
- [ ] Set up GitHub Pages (docs site)
### Day 5
- [ ] Write 3 documentation pages (install, usage, comparison)
- [ ] Add license key validation to `mcp_server.py`
- [ ] Create upgrade prompts (demo → paid)
### Day 6
- [ ] Publish Gumroad products (Extended Cache, Live API)
- [ ] Test end-to-end purchase flow
- [ ] Create provisioning script for Spectator keys
### Day 7
- [ ] Post to Hacker News ("Show HN")
- [ ] Post to r/ClaudeAI
- [ ] Monitor installs + feedback
---
## 📚 Appendix: Technical Specs
### Extended Cache File Structure
```
financial_data.parquet (50MB compressed)
├─ Schema:
│ ├─ ticker (string)
│ ├─ metric (string)
│ ├─ value (float64)
│ ├─ period (string)
│ ├─ filing_type (string)
│ ├─ filing_url (string)
│ ├─ consistency_score (float32)
│ └─ as_of (datetime64)
├─ Rows: ~250,000 (500 tickers × 10 metrics × 5 years × 4 quarters)
├─ Compression: Snappy (parquet native)
└─ Update Frequency: Quarterly
```
### Spectator Query API
```python
# sec_query.py (on Spectator)
import sys
import json
from sec_scraper import SECEdgar
ticker = sys.argv[1]
metric = sys.argv[2]
scraper = SECEdgar()
result = scraper.get_metric(ticker, metric)
print(json.dumps(result))
```
**Response Format:**
```json
{
"ticker": "AAPL",
"metric": "revenue_ttm",
"value": 383285000000,
"unit": "USD",
"period": "TTM",
"as_of": "2024-09-30",
"sources": [{
"type": "sec_filing",
"filing": "10-K",
"url": "https://www.sec.gov/...",
"excerpt": "Total net sales: $383,285 million"
}],
"consistency_score": 0.96,
"retrieved_at": "2026-01-21T11:00:00Z"
}
```
---
## 🎬 Closing
**This is your path to $10k/mo:**
1. **Week 1-2:** Build cache, polish product, optimize listings
2. **Week 3-4:** Launch on Hacker News, MCP directory, r/ClaudeAI
3. **Week 5-8:** Ride the install wave, iterate on conversion funnel
4. **Week 9-12:** Scale with SEO, affiliates, enterprise outreach
**No servers. No community management. Just product + distribution.**
The architecture is serverless (Gumroad + Spectator), the moat is citations, and the market is every Claude Desktop user who needs financial data.
**Execute. Ship. Scale.**
---
**Masterplan Version:** 1.0
**Created:** 2026-01-21
**Owner:** Christopher Ongko
**Next Review:** 2026-02-21 (30 days)
