Artefact Revenue Intelligence MCP Server

The AI-native interface to your Revenue Operating System. Version-controlled GTM intelligence — signals, commits, and closed-loop measurement — accessible to any AI agent.

A Model Context Protocol (MCP) server that treats your Go-to-Market strategy like code: versioned, diffable, and deployable. Detect pipeline signals, identify scaling constraints, analyze value engines, and draft structured GTM changes — all through AI-native tool calls. Built on the Artefact Formula methodology from real B2B consulting engagements.

Why Artefact MCP?

Traditional ICP models stop at firmographics. We triangulate across three dimensions to identify prospects with the right profile, the right behaviors, AND the right trajectory.

Who Is This For?

• B2B revenue teams using HubSpot who want AI-powered signal detection and pipeline intelligence
• RevOps managers who need constraint analysis and value engine health accessible from Claude or Cursor
• Consultants who deliver RFM analysis, ICP scoring, and evidence-backed GTM recommendations to clients
• Developers building revenue intelligence integrations with MCP
• AI agents that need a structured interface to reason about and propose changes to GTM strategy

Tools

Signal Intelligence

detect_signals — Pipeline Signal Detection

Scans pipeline data for all 6 signal types from the Artefact signal taxonomy: velocity anomalies, conversion drop-offs, win/loss patterns, pipeline concentration, data quality issues, and SPICED frequency signals. Returns structured signal objects with strength scores (0-1), evidence, and recommended actions.

identify_constraint — Dominant Constraint Analysis

Identifies which of the 4 scaling constraints (Lead Generation, Conversion, Delivery, Profitability) is bottlenecking revenue. Includes Revenue Formula breakdown (Traffic x CR1 x CR2 x CR3 x ACV) with gap-to-benchmark analysis and recommended focus.

analyze_engine — Value Engine Health

Analyzes health of the 3 value engines: Growth (create/capture/convert demand), Fulfillment (onboard/deliver/renew/expand), and Innovation (gather/prioritize/build/launch). Returns engine-specific metrics, health scores, and integrated signal detection.

propose_gtm_change — GTM Commit Drafting

Enables AI agents to propose structured GTM changes following the commit anatomy: Intent, Diff, Impact Surface, Risk Level, Evidence, and Measurement Plan. Supports 8 entity types (ICP, persona, positioning, pipeline stage, exit criteria, GTM motion, scoring model, playbook).

Analysis Tools

run_rfm — RFM Analysis

Scores clients on Recency, Frequency, and Monetary value. Segments them into 11 ca

... View full README on GitHub