The GitDealFlow MCP server lets Claude, Cursor, and other AI assistants query deal flow data directly. Instead of copy-pasting data into a chatbot, you ask the AI to fetch and analyze deals.
Install: run `npx -y @gitdealflow/mcp-signal` in Claude Desktop or Cursor. The server exposes six tools: get_trending_startups, search_startups_by_sector, get_startup_signal, get_signals_summary, get_scout_receipts, and get_methodology.
Workflow example: 'Find trending AI infrastructure startups with rising momentum, then draft outreach emails to the top 3.' Claude queries GitDealFlow, ranks by momentum, and drafts personalized notes referencing each startup's engineering traction.
For scouts, the get_scout_receipts tool computes a Scout Score (0-100) for any GitHub username based on their starring history vs. ~75 validated unicorns. High Scout Scores predict which developers will found valuable companies.
The steps above are not generic advice; they are how the GitDealFlow dataset is used in practice. The underlying data covers 350+ startup GitHub organizations in 15 sectors, refreshed weekly, with breakouts surfacing 21 to 47 days before rounds are announced. Every workflow here compresses to the same loop: pull the signal, confirm it with a second window, qualify it against sector context, then act while the round is still quiet.
A practical read-through of How to Use the GitDealFlow MCP Server for VC Research: the dataset behind this page refreshes weekly across 350+ organizations and 15 sectors, and every figure shown traces to a public GitHub REST API pull. That matters for two reasons. Reproducibility: any number here can be re-derived from primary sources, which is the standard the published methodology sets for itself. Timeliness: engineering acceleration precedes announcements, so this page follows the data cadence rather than the news cycle, and the freshness endpoint always reports the exact pull date.
If How to Use the GitDealFlow MCP Server for VC Research is your entry point, the fastest next steps are fixed: skim the glossary for the three or four terms that anchor the topic, open the research dataset to see the raw weekly snapshots behind the summary numbers, and run one live query against the free momentum checker with a company you already know well. Seeing the signal fire on a familiar name is the quickest way to judge whether code-side sourcing belongs in your own workflow.