Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
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.
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.
Related MCP server: Sales Signals MCP Server
Tool | What it returns |
| Signal strength, outreach timing, pitch angle, pitfalls, decision window for hiring events (new exec, team expansion) |
| Same for funding events (seed → IPO, down rounds) including budget bands and typical buyers |
| Same for tech-stack changes (added/removed competitor, warehouse adoption, compliance tooling) |
| Same for C-suite changes (CEO/CFO/CTO/CMO/founder departures) |
| Same for market expansion (international office, vertical, product launch) |
| 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 windowApify Pay-Per-Event: $0.05 per tool call
First 10 calls free per actor
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
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
Returns the complete signal-interpretation library + metadata. Useful for fine-tuning or full agent context.
Inputs: no named parameters.
Interpret a market expansion signal (new geo, vertical, product). Returns implications and outreach playbook. Types: international_office_opening, vertical_expansion, product_launch.
Inputs: expansion_type (required).
Interpret a funding-event signal. Returns interpretation, budget band, typical buyers, outreach timing, pitfalls. Stages: seed, series_a, series_b, series_c_plus, ipo_recent, down_round.
Inputs: funding_stage (required).
Interpret a hiring signal (new exec hire, team expansion, role posting). Returns signal strength, outreach timing, pitch angle, common pitfalls, average decision window. Provide signal_type from: head_of_sales, head_of_revenue, head_of_security, vp_marketing, sdr_team_expansion, head_of_data.
Inputs: signal_type (required).
Interpret a leadership-change signal. Returns interpretation, outreach timing, pitch angle. Types: ceo_change, cfo_change, cto_change, cmo_change, founder_departure.
Inputs: change_type (required).
Interpret a tech-stack change signal. Returns interpretation, outreach timing, pitch angle, decision window. Types: added_competitor, removed_competitor, added_warehouse, added_compliance_tool, removed_legacy_crm.
Inputs: change_type (required).
Given a list of signals observed for a target account, return a composite intent score (0-100) and recommended outreach action. Use this to prioritize an account list.
Inputs: signals (required).
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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Scored across 7 tools
Each tool targets a distinct signal type (funding, hiring, expansion, leadership, tech stack) plus a composite scoring tool and a full pack retrieval. No overlap in purposes.
Five tools follow the consistent 'interpret_<signal_type>' pattern, but 'get_full_pack' and 'score_buyer_intent' break the pattern with different verbs, causing minor inconsistency.
Seven tools is well-scoped for interpreting key B2B buyer signals and scoring intent. Not overly numerous nor sparse.
Covers major signal types and includes aggregation via scoring. Minor gap: lacks coverage for signals like partnership or regulatory changes, but core workflows are complete.
Market & Company Intelligence for AI agents: next adoptions, company health, signals, market feeds.
Signal-based B2B prospecting: find leads with buying intent, run email and LinkedIn outreach.
Find buying signals for B2B leads and write personalized cold-email icebreakers.
B2B leads at a buying moment, with the why and the outreach. Ask your AI about your leads.