B2B Buyer-Signal MCP

B2B Buyer-Signal MCP

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.

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B2B Buyer-Signal MCP

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.


Why This Exists

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.

6 Tools

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

Sample Use

// 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

Pricing

  • Apify Pay-Per-Event: $0.05 per tool call
  • First 10 calls free per actor

Production Roadmap

This v1.0 is the interpretation layer. Future versions:

  • v1.1: Multi-signal correlation patterns (e.g., "head_of_sales + sdr_team_expansion within 30 days = pre-Series-A signal")
  • v1.2: Industry-specific weightings (SaaS vs Fintech vs Healthcare have different signal half-lives)
  • v1.3: Time-decay scoring (signal age affects weight)
  • v2.0: Optional bring-your-own-data adapter for Clay / Apify Scrapers / Crunchbase API

Built By

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

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