From company name to investment decision. Sieve scores startups across 7 dimensions so you know who's worth a meeting.
Project description
Sieve MCP Server
From company name to investment decision. Sieve scores startups across 7 dimensions so you know who's worth a meeting.
An MCP server that brings AI-powered startup due diligence into Claude, Cursor, Windsurf, and any MCP-compatible assistant. Screen any startup, get a quantified Sieve Score (0-140), and a clear Take Meeting / Pass recommendation.
Quick Start
Claude Desktop
Add to your claude_desktop_config.json:
{
"mcpServers": {
"sieve": {
"command": "uvx",
"args": ["sieve-mcp"],
"env": {
"SIEVE_API_KEY": "your-api-key"
}
}
}
}
Claude Code
claude mcp add sieve -- uvx sieve-mcp
Then set your API key in the environment:
export SIEVE_API_KEY="your-api-key"
Cursor / Windsurf
Add to your MCP settings:
{
"mcpServers": {
"sieve": {
"command": "uvx",
"args": ["sieve-mcp"],
"env": {
"SIEVE_API_KEY": "your-api-key"
}
}
}
}
Environment Variables
| Variable | Required | Default | Description |
|---|---|---|---|
SIEVE_API_KEY |
Yes | — | Your Sieve API key. Get one at sieve.arceusxventures.com/settings |
SIEVE_API_URL |
No | https://api.sieve.arceusxventures.com |
API base URL (override for self-hosted) |
Available Tools
| Tool | Description | Read-only |
|---|---|---|
sieve_screen |
Run a Quick Screen on a startup | No |
sieve_status |
Check analysis progress | Yes |
sieve_summary |
Get full results of a completed analysis | Yes |
sieve_usage |
Check API usage for current billing period | Yes |
Example Usage
A typical workflow in any MCP-compatible AI assistant:
-
Screen a startup:
"Run a Sieve screen on Acme Corp at acme.com"
-
Poll for progress (analysis takes 2-5 minutes):
"Check the status of that Sieve analysis"
-
Get results:
"Show me the Sieve summary for that analysis"
-
Check usage:
"How many Sieve screens do I have left this month?"
Running with Docker
docker build -t sieve-mcp .
docker run -p 8080:8080 -e SIEVE_API_KEY=your-key sieve-mcp
Development
# Install locally
pip install -e .
# Run in stdio mode (for MCP clients)
sieve-mcp
# Run in HTTP mode (for remote/container deployment)
sieve-mcp http
License
MIT
Project details
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