Example
API and MCP Platform for Turning Research Papers into Buildable Product Signals.
This example is the cleanest demonstration of the proof layer working as designed: a durable topic page feeds a citation-first Signal Canvas answer, then the same context can continue into workspaces or Build Loop.
Workflow
Proof Surface Example
Durable research-area page with paper counts, trend direction, authors, and top questions.
Open proof surfacecurl
curl -X POST "https://sciencetostartup.com/api/mcp" \
-H "Authorization: Bearer s2s_YOUR_KEY" \
-H "Content-Type: application/json" \
-d '{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/call",
"params": {
"name": "search_signal_canvas",
"arguments": {"query":"What should I commercialize next in llm analysis?","mode":"corpus"}
}
}'Python
import requests
payload = {
"jsonrpc": "2.0",
"id": 1,
"method": "tools/call",
"params": {
"name": "search_signal_canvas",
"arguments": {"query":"What should I commercialize next in llm analysis?","mode":"corpus"},
},
}
response = requests.post(
"https://sciencetostartup.com/api/mcp",
headers={
"Authorization": "Bearer s2s_YOUR_KEY",
"Content-Type": "application/json",
},
json=payload,
timeout=30,
)TypeScript
const response = await fetch("https://sciencetostartup.com/api/mcp", {
method: "POST",
headers: {
Authorization: "Bearer s2s_YOUR_KEY",
"Content-Type": "application/json",
},
body: JSON.stringify({
jsonrpc: "2.0",
id: 1,
method: "tools/call",
params: {
name: "search_signal_canvas",
arguments: {"query":"What should I commercialize next in llm analysis?","mode":"corpus"},
},
}),
});