About B2B Buyer-Signal MCP
B2B Buyer-Signal MCP is an MCP server published by closermethod in the Communication category: 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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Install
git clone https://github.com/closermethod/buyer-signal-mcpThis server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.
Tools (6)
interpret_expansion_signalSame for market expansion (international office, vertical, product launch)
Parameters not published.
interpret_funding_signalSame for funding events (seed → IPO, down rounds) including budget bands and typical buyers
Parameters not published.
interpret_hiring_signalSignal strength, outreach timing, pitch angle, pitfalls, decision window for hiring events (new exec, team expansion)
Parameters not published.
interpret_leadership_changeSame for C-suite changes (CEO/CFO/CTO/CMO/founder departures)
Parameters not published.
interpret_tech_stack_changeSame for tech-stack changes (added/removed competitor, warehouse adoption, compliance tooling)
Parameters not published.
score_buyer_intentComposite intent score (0-100) given multiple signals — for prioritization
Parameters not published.
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README
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
README copy captured when this server was indexed; it may lag the repository. The original is authoritative.