How do I use B2B Buyer-Signal MCP?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@B2B Buyer-Signal MCP Interpret hiring signal: new Head of Sales at Acme" 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.

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