Frontier Deal Screener
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Frontier Deal Screenerscore Classiq using the default rubric"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Frontier Deal Screener — MCP Server
A free, self-contained MCP server that serves a curated quantum / deep-tech startup dataset (~34 companies) and a weighted diligence rubric, from a neutral investor/acquirer vantage. No external API, no API keys, no accounts — all data and scoring run locally.
It's the queryable version of the Frontier Deal Screener web tool: instead of a web page, Claude (or any MCP client) can call it directly — "list the interconnect startups", "score Classiq", "rate a deal with these numbers".
Company profiles are illustrative estimates from public information, not assessments of those companies. Weights, tiers, and posture rules are transparent defaults — adjust them to your own thesis (edit
screener_data.py).
Webapp here: https://khorovitz.github.io/quantumdeals/
Tools
Tool | What it does |
| List/filter the dataset by layer, stage, posture, or min conviction |
| Full profile + computed rating + source for one company |
| Score arbitrary inputs → conviction, tier, posture, rationale |
| The dimensions, weights, 0/3/5 anchors, tiers, risk model, sources |
Related MCP server: Financial Risk MCP Server
Files
quantum_deal_screener_mcp.py— the MCP server (tools)screener_data.py— the dataset + scoring logic (dependency-free; edit this to tune)requirements.txt— the one dependency (mcp)
Setup
git clone https://github.com/<your-username>/quantum-deal-screener-mcp.git
cd quantum-deal-screener-mcp
python3 -m venv .venv && source .venv/bin/activate # optional but recommended
pip install -r requirements.txt
python3 quantum_deal_screener_mcp.py # runs on stdio; Ctrl-C to stopRequires Python 3.10+.
(There's nothing to see when you run it directly — it waits for an MCP client to connect over stdio. Point a client at it as below.)
Connect it to Claude
This is a local stdio server. Add it to whichever client you use, replacing the paths with the absolute paths on your machine (and the venv's python if you made one).
Claude Desktop / Claude Code — add to your MCP config (e.g. claude_desktop_config.json,
or via Claude Code's MCP settings):
{
"mcpServers": {
"quantum-deal-screener": {
"command": "/absolute/path/to/qds_mcp/.venv/bin/python3",
"args": ["/absolute/path/to/qds_mcp/quantum_deal_screener_mcp.py"]
}
}
}If you didn't make a venv, use your system python3 as the command (make sure
pip install mcp ran for that interpreter).
Test without a client with the MCP Inspector:
npx @modelcontextprotocol/inspector python3 quantum_deal_screener_mcp.pyTune it
Everything lives in screener_data.py:
COMPANIES— add/remove companies or change any profileDIMS— dimension weights and 0/3/5 anchorsTIERS— the tier thresholdsdecide()— the posture logic (acquire/partner/invest/watch/pass)
Change those and the tools reflect it immediately on restart — no other edits needed.
License
MIT — see LICENSE. The bundled company profiles are illustrative estimates from public information for research/education, not investment advice or assessments of the named companies.
This server cannot be deployed
Maintenance
Related MCP Connectors
MCP server for VC pitch-deck scoring, thesis-fit matching, and deal-flow management.
AI research on companies and industries — one MCP tool per research domain.
Curated investor database: 10,469 VC, angel, PE, and family office firms with contacts, via MCP.
Agentic fundraising intelligence: 1100+ tracked VC funds and 721 disclosed LPs over MCP
Related MCP Servers
FlicenseNot gradedqualityDmaintenanceEnables dealmaking research for AI assistants, providing company intelligence, transaction data, and research deliverables via MCP.-- FlicenseNot gradedqualityCmaintenanceEnables portfolio risk assessment, sentiment analysis, and investment recommendations via MCP tools.-
- AlicenseNot gradedqualityAmaintenanceProvides startup valuation methods with auditable calculations, readiness checks, and explanations through MCP tools.MIT
- AlicenseNot gradedqualityBmaintenanceEnables MCP-compatible AI agents to retrieve quantum-computed portfolio optimisation, VaR simulations, AI-enhanced sentiment and regime detection, and cross-asset risk signals through simple metric tools.25 npmMIT