ScienceToStartup
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Seattle, WA 98104-2205

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ScienceToStartup

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Use Case

ScienceToStartup is an Agent Operating System for Research Commercialization

API and MCP Platform for Turning Research Papers into Buildable Product Signals.

This is the shortest path when you want ChatGPT, Cursor, Claude, or a custom agent to search research papers, inspect proof surfaces, and preserve source context without hand-writing a large REST adapter.

Create KeyMCP GuideMCP Endpoint
Use remote MCP when you want tool-native discovery with less client code.
Preserve paper IDs, source context, and proof links from search into execution.
Pair MCP discovery with Build Loop or workspace actions after paper selection.

Workflow

Query-led flow

  1. 1Initialize the remote MCP endpoint and authenticate with a developer key.
  2. 2Call search_papers or search_signal_canvas for a research question.
  3. 3Read the returned proof surface and persist the source context into your agent state.
  4. 4Use workspace or launch-pack actions only after a paper or topic is selected.

Proof Surface Example

Example paper page

Stable evidence receipt, viability score, citations, and execution handoffs on one public example URL.

Open proof surface

curl

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_papers",
      "arguments": {"query":"voice agents","limit":5}
    }
  }'

Python

import requests

payload = {
    "jsonrpc": "2.0",
    "id": 1,
    "method": "tools/call",
    "params": {
        "name": "search_papers",
        "arguments": {"query":"voice agents","limit":5},
    },
}

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_papers",
      arguments: {"query":"voice agents","limit":5},
    },
  }),
});

Canonical links

OpenAPI

Machine-readable REST contract for search, proof retrieval, Signal Canvas, and exports.

MCP vs REST

See when tool-native access beats direct HTTP integrations.

Cursor setup

Install the same MCP endpoint into Cursor.

See the live proof surface

Example paper page

Stable evidence receipt, viability score, citations, and execution handoffs on one public example URL.

Signal Canvas

Citation-first answer surface that turns paper context into research-to-product judgment.

Topic proof layer

Durable research-area page with paper counts, trend direction, authors, and top questions.

Benchmark scoreboard

Weekly ranking surface for high-signal papers and ranked commercialization comparisons.

Public dataset

Machine-readable export surface for papers, scores, tags, and canonical identifiers.

Build Loop

Execution workspace that takes proof context into verify, export, and launch-pack actions.

Stable contracts and discovery files

Official SDKs

JavaScript and Python client surfaces plus example quickstarts for REST, MCP, and workspace flows.

OpenAPI

Machine-readable REST contract for search, proof retrieval, Signal Canvas, and exports.

MCP endpoint

Remote MCP server for discovery tools, resources, and workspace actions.

Capability index

Product graph covering proof surfaces, install paths, and canonical action flows.

llms.txt

Short routing file for agents choosing between proof, developer, and install surfaces.

llms-full.txt

Exhaustive corpus and contract inventory for retrieval systems and agent builders.