Commercial launch: Postgres auth, BTCPay payments, premium UI

- Postgres: users, api_keys, usage_log, payments tables
- Auth: API key system with rate limiting (402 when out of calls)
- BTCPay:  Bitcoin = 5 API calls, webhook for auto-credit
- Admin: sk-admin-unlimited-2026 with unlimited calls
- UI: Landing, Pricing (/5calls), Signup (no KYC), Dashboard with usage stats
- Footer: Created by drjones + Buy Me a Coffee link
- All endpoints auth-gated except signup, status, pages

server.py

@@ -1,110 +1,81 @@
 #!/usr/bin/env python3
-"""
-AI Research Engine — Thin MCP Proxy
-Runs on MacBook. Forwards all tool calls to the CT 145 backend.
-Minimal resource usage — all heavy lifting on Proxmox.
-"""
+"""AI Research Engine — Thin MCP Proxy. Admin key for backend auth."""
 
 import json
 import httpx
 from mcp.server import FastMCP
 
 BACKEND_URL = "http://10.30.20.249:8000"
+ADMIN_KEY = "sk-admin-unlimited-2026"
 client = httpx.Client(timeout=120.0)
 
-mcp = FastMCP(
-    "ai-research-engine",
-    instructions="""
-AI Research Engine — private knowledge acquisition system.
-
-search_web(query) — Full-text search across indexed documents
-semantic_search(query) — Find documents by meaning (vector search)
-crawl_url(url) — Crawl a URL into the index
-crawl_topic(topic) — Discover and crawl sources for a topic
-research_topic(topic) — Full pipeline: discover → crawl → summarize
-retrieve_document(url) — Get full content of an indexed document
-summarize_sources(urls, instruction) — AI summary of multiple sources
-extract_information(url, schema) — Structured data extraction
-create_report(topic, sources) — Generate comprehensive research report
-index_status() — System health and stats
-""",
-)
-
 def _get(path: str) -> dict:
-    r = client.get(f"{BACKEND_URL}{path}")
+    sep = "&" if "?" in path else "?"
+    r = client.get(f"{BACKEND_URL}{path}{sep}api_key={ADMIN_KEY}")
     r.raise_for_status()
     return r.json()
 
+mcp = FastMCP("ai-research-engine", instructions="AI Research Engine — private knowledge acquisition. 10 tools.")
 
 @mcp.tool()
 def search_web(query: str, category: str = "", limit: int = 10) -> str:
-    """Full-text search across indexed documents. Find by keywords, titles, content."""
+    """Full-text search across indexed documents."""
     r = _get(f"/api/search?q={query}&category={category}&limit={limit}")
     return json.dumps(r, indent=2)
 
-
 @mcp.tool()
 def semantic_search(query: str, limit: int = 10) -> str:
-    """Search by meaning using vector embeddings. Finds conceptually related docs."""
+    """Search by meaning using vector embeddings."""
     r = _get(f"/api/semantic-search?q={query}&limit={limit}")
     return json.dumps(r, indent=2)
 
-
 @mcp.tool()
 def crawl_url(url: str, depth: int = 1) -> str:
-    """Crawl a URL. depth: 0=just this page, 1=+linked pages."""
+    """Crawl a URL and index it."""
     r = _get(f"/api/crawl?url={url}&depth={depth}")
     return json.dumps(r, indent=2)
 
-
 @mcp.tool()
 def crawl_topic(topic: str, max_urls: int = 20) -> str:
-    """Discover and crawl sources for a topic using YaCy."""
+    """Discover and crawl sources for a topic."""
     r = _get(f"/api/crawl-topic?topic={topic}&max_urls={max_urls}")
     return json.dumps(r, indent=2)
 
-
 @mcp.tool()
 def research_topic(topic: str) -> str:
-    """Full research pipeline: keyword search → semantic search → crawl new sources → AI summary."""
+    """Full pipeline: search → crawl → AI summary."""
     r = _get(f"/api/research?topic={topic}")
     return json.dumps(r, indent=2)
 
-
 @mcp.tool()
 def retrieve_document(url: str) -> str:
-    """Get full indexed content of a document by URL."""
+    """Get full indexed content of a document."""
     r = _get(f"/api/document?url={url}")
     return json.dumps(r, indent=2)
 
-
 @mcp.tool()
 def summarize_sources(urls: str, instruction: str = "Summarize key points") -> str:
-    """Summarize multiple URLs using local LLM. urls: comma-separated."""
+    """AI summary of multiple URLs. urls: comma-separated."""
     r = _get(f"/api/summarize?urls={urls}&instruction={instruction}")
     return json.dumps(r, indent=2)
 
-
 @mcp.tool()
-def extract_information(url: str, schema: str = "company names, products, prices, specifications") -> str:
-    """Extract structured information from a document using LLM."""
+def extract_information(url: str, schema: str = "company names, products, prices") -> str:
+    """Extract structured data from a document using LLM."""
     r = _get(f"/api/extract?url={url}&schema={schema}")
     return json.dumps(r, indent=2)
 
-
 @mcp.tool()
 def create_report(topic: str, sources: str = "") -> str:
-    """Generate a comprehensive research report. sources: optional comma-separated URLs."""
+    """Generate comprehensive research report."""
     r = _get(f"/api/report?topic={topic}&sources={sources}")
     return json.dumps(r, indent=2)
 
-
 @mcp.tool()
 def index_status() -> str:
-    """Check health of all backend services: OpenSearch, Qdrant, YaCy, Ollama."""
+    """System health + business stats."""
     r = _get("/api/status")
     return json.dumps(r, indent=2)
 
-
 if __name__ == "__main__":
     mcp.run(transport="stdio")