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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.

glama
Updated
May 4, 2026

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

ToolWhat it returns
interpret_hiring_signalSignal strength, outreach timing, pitch angle, pitfalls, decision window for hiring events (new exec, team expansion)
interpret_funding_signalSame for funding events (seed → IPO, down rounds) including budget bands and typical buyers
interpret_tech_stack_changeSame for tech-stack changes (added/removed competitor, warehouse adoption, compliance tooling)
interpret_leadership_changeSame for C-suite changes (CEO/CFO/CTO/CMO/founder departures)
interpret_expansion_signalSame for market expansion (international office, vertical, product launch)
score_buyer_intentComposite 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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