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Interprets B2B buyer signals (hiring, funding, tech changes) into structured outreach implications for AI sales agents, bridging the gap between raw signal data
Interprets B2B buyer signals (hiring, funding, tech changes) into structured outreach implications for AI sales agents, bridging the gap between raw signal data and actionable intent.
Intent layer for AI sales agents — structured signal interpretation, not data scraping.
Built from 10+ years of B2B enterprise sales experience.
Disclaimer. Outputs are structured signal-interpretation frameworks based on publicly-documented B2B sales practice. Not investment, financial, or legal advice. Not a substitute for human qualification. Verify any specific claim about a company, person, or event with primary sources before outreach.
Every AI SDR and sales agent today has the same structural gap: they have signal data (from Apollo, Clay, Crunchbase, scrapers, paid APIs) but no consistent interpretation layer. They know a target hired a Head of Sales, but they don't know what to do with that information.
This MCP bridges that gap. Provide a signal payload — receive structured outreach implications.
It does NOT scrape data sources. Use Apify / Clay / Apollo / Crunchbase / LinkedIn ecosystem for upstream collection. This MCP is the interpretation engine.
| Tool | What it returns |
|---|---|
interpret_hiring_signal |
Signal strength, outreach timing, pitch angle, pitfalls, decision window for hiring events (new exec, team expansion) |
interpret_funding_signal |
Same for funding events (seed → IPO, down rounds) including budget bands and typical buyers |
interpret_tech_stack_change |
Same for tech-stack changes (added/removed competitor, warehouse adoption, compliance tooling) |
interpret_leadership_change |
Same for C-suite changes (CEO/CFO/CTO/CMO/founder departures) |
interpret_expansion_signal |
Same for market expansion (international office, vertical, product launch) |
score_buyer_intent |
Composite intent score (0-100) given multiple signals — for prioritization |
// AI agent observes: "Acme just hired a new Head of Sales last week + announced Series B"
// Calls:
mcp.call("interpret_hiring_signal", { signal_type: "head_of_sales" });
mcp.call("interpret_funding_signal", { funding_stage: "series_b" });
mcp.call("score_buyer_intent", { signals: ["head_of_sales", "series_b"] });
// Returns: tier, recommended action, pitch angle, decision window
This v1.0 is the interpretation layer. Future versions:
Elisabeth Hitz — 10+ years of B2B enterprise sales experience across ad-tech, SaaS, media, and global hiring. Five-year stretch overshooting quota at a publicly-listed ad-tech company. Now building MCP servers for the AI agent ecosystem.
License: MIT
Add this to claude_desktop_config.json and restart Claude Desktop.
{
"mcpServers": {
"b2b-buyer-signal-mcp": {
"command": "npx",
"args": []
}
}
}