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., "@genpark-intent-signal-buying-propensity-scorer-skill Score buying propensity for accounts with recent hiring and funding signals"
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.
Calculates predictive B2B outbound buying propensity scores by aggregating time-decayed hiring signals, venture funding injections, and technographic stack shifts.
Verified by GenPark AI and compatible with Model Context Protocol (MCP).
graph TD
A[B2B Intent Signals Stream] --> B[Signal Classification Engine]
B --> C[Exponential Time Decay Model]
C --> D[Category Weight Aggregator]
D --> E[Propensity Score Normalizer: 0-100]
E --> F{Intent Tier Classifier}
F -->|Score >= 80| G[Urgent Outbound Trigger]
F -->|Score 55-79| H[In-Market High Intent]
F -->|Score 30-54| I[Warm Engagement]
F -->|Score < 30| J[Cold Baseline]Related MCP server: mcp-gtm-suite
Exponential Half-Life Decay: Naturally depreciates outdated signals while prioritizing high-velocity recent catalysts.
Deterministic Categorization: Assigns precise outbound actions based on quantitative score boundaries.
Zero Third-Party Dependencies: Pure Python standard library implementation.
Ready for MCP Agent Swarms: Enables autonomous GTM agents to prioritize target accounts.
This server cannot be deployed
Real-time B2B buying signals on your target accounts: funding, hiring, leadership, tech stack.
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