Skip to main content
Glama
khorovitz

Frontier Deal Screener

by khorovitz

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

screener_list_companies

List/filter the dataset by layer, stage, posture, or min conviction

screener_get_company

Full profile + computed rating + source for one company

screener_rate_deal

Score arbitrary inputs → conviction, tier, posture, rationale

screener_methodology

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 stop

Requires 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.py

Tune it

Everything lives in screener_data.py:

  • COMPANIES — add/remove companies or change any profile

  • DIMS — dimension weights and 0/3/5 anchors

  • TIERS — the tier thresholds

  • decide() — 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.

Related MCP Connectors

Related MCP Servers

  • F
    license
    Not graded
    quality
    D
    maintenance
    Enables dealmaking research for AI assistants, providing company intelligence, transaction data, and research deliverables via MCP.
    -
  • A
    license
    Not graded
    quality
    A
    maintenance
    Provides startup valuation methods with auditable calculations, readiness checks, and explanations through MCP tools.
    MIT
  • A
    license
    Not graded
    quality
    B
    maintenance
    Enables 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.
    33 npm
    MIT