Sieve — Startup Due Diligence
@lmwharton
Sieve — Startup Due Diligence について
AI-powered startup due diligence. Screen any company across 7 IMPACT-X dimensions (Team, Market, Product, Advantage, Commerce, Traction, X-Factor) and get a quantified Sieve Score (0-140) with a Take Meeting / Pass / Need More Info recommendation. From company name to investment
基本情報
設定
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"sieve": {
"command": "uvx",
"args": [
"sieve-mcp"
],
"env": {
"SIEVE_API_KEY": "your-api-key-here"
}
}
}
}ツール
ツールは検出されませんでした
ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。
概要
What is Sieve — Startup Due Diligence?
Sieve — Startup Due Diligence is an AI-powered tool that screens any company across seven IMPACT-X dimensions (Team, Market, Product, Advantage, Commerce, Traction, X-Factor). It returns a quantified Sieve Score from 0 to 140 along with a Take Meeting, Pass, or Need More Info recommendation, enabling investors to go from company name to an investment decision in minutes.
How to use Sieve — Startup Due Diligence?
Provide the name of a startup company. The server processes the input and delivers a due diligence report containing a combined Sieve Score and a clear recommendation.
Key features of Sieve — Startup Due Diligence
- AI-powered startup due diligence
- Screens across 7 IMPACT-X dimensions
- Quantified Sieve Score (0–140)
- Three recommendation outcomes: Take Meeting, Pass, Need More Info
- Fast: from company name to decision in minutes
Use cases of Sieve — Startup Due Diligence
- Quick screening of early-stage startups for investment
- Comparing multiple companies across standardized dimensions
- Preliminary due diligence before deeper analysis
- Portfolio monitoring and re-evaluation
FAQ from Sieve — Startup Due Diligence
What does the Sieve Score mean?
The Sieve Score is a quantified rating from 0 to 140 based on the analysis across all seven IMPACT-X dimensions.
What are the IMPACT-X dimensions?
The seven dimensions are Team, Market, Product, Advantage, Commerce, Traction, and X-Factor.
What recommendations does the server provide?
The server outputs one of three recommendations: Take Meeting, Pass, or Need More Info.
How does the server process a company name?
It uses AI to analyze the input company name and returns a due diligence assessment with a score and recommendation.
「その他」の他のコンテンツ
Core Philosophy: Connect, Unify, Respond
mindsdbDelegate anything. It comes back done.
Codelf
unbugA search tool helps dev to solve the naming things problem.
Nginx UI
0xJackyYet another WebUI for Nginx
Awesome Mlops
visengerA curated list of references for MLOps
🚀 Model Context Protocol (MCP) Curriculum for Beginners
microsoftThis open-source curriculum introduces the fundamentals of Model Context Protocol (MCP) through real-world, cross-language examples in .NET, Java, TypeScript, JavaScript, Rust and Python. Designed for developers, it focuses on practical techniques for building modular, scalable,
コメント