artefact-mcp-server

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.

Feature	HubSpot Official MCP	Generic Wrappers	Artefact MCP
CRUD operations	Yes	Yes	Via HubSpot API
RFM Analysis	No	No	11-segment classification
ICP Triangulation	No	No	Firmographic + Behavioral + Growth Signals
Pipeline Health	No	No	0-100 health score + exit criteria testing
Signal Detection	No	No	6-type signal taxonomy
Constraint Analysis	No	No	Dominant bottleneck + Revenue Formula
Value Engine Analysis	No	No	Growth / Fulfillment / Innovation
GTM Commit Drafting	No	No	Structured change proposals with evidence
Methodology built-in	No	No	Artefact Formula (10 resources)
Works without API key	No	No	Yes (demo data)

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 categories (Champions through Lost) and extracts ICP patterns from top performers. Now includes signal framing — detects win/loss patterns, revenue concentration, and at-risk client signals. Supports B2B service, SaaS, and manufacturing presets.

qualify — ICP Triangulation Framework

Scores prospects across three dimensions: Firmographic Fit (industry, revenue, employees, geography), Behavioral Fit (tech stack, engagement, purchase history), and Growth Signals (hiring, funding, expansion). Now includes constraint context — maps prospect fit to your dominant scaling constraint. Returns tier classification (Ideal / Strong / Moderate / Poor) with engagement strategy.

score_pipeline_health — Pipeline Health Score

Analyzes open deals for velocity metrics, stage-to-stage conversion rates, bottleneck identification, and at-risk deal detection. Now supports optional exit criteria testing (pass/fail per criterion per deal) and includes signal framing for velocity anomalies and conversion drop-offs. Returns a 0-100 health score.

Resources

URI	Description
methodology://scoring-model	ICP Triangulation Framework technical reference
methodology://tier-definitions	4-tier classification system
methodology://rfm-segments	11 RFM segment definitions with scoring scales
methodology://spiced-framework	SPICED discovery framework
methodology://data-requirements	HubSpot data setup and enrichment requirements
methodology://value-engines	3 value engine definitions (Growth, Fulfillment, Innovation) with stages and metrics
methodology://exit-criteria	Standard pipeline exit criteria per stage with proof requirements
methodology://constraints	4 scaling constraints with diagnostic criteria and remediation levers
methodology://signal-taxonomy	6 signal types with detection methods and action mappings
methodology://revenue-formula	Revenue Formula breakdown: Traffic x CR1 x CR2 x CR3 x ACV x (1/Churn)
methodology://gtm-commit-anatomy	5 components of a structured GTM commit (intent, diff, impact, risk, evidence)

Data Requirements for ICP Triangulation

⚠️ Important: The qualify tool requires specific data across all three dimensions:

✅ Native HubSpot data (Firmographic + Partial Behavioral):

• Firmographic Fit: Industry, revenue, employees, geography — standard properties
• Behavioral Fit (Partial): Tech stack, content engagement, purchase history — custom properties or workflows

⚠️ Requires external enrichment (Clay, Clearbit, or manual research):

• Growth Signals (Behavioral Fit — Critical Dimension): Hiring trends, funding rounds, product launches, expansion signals, press mentions
• HubSpot does NOT track growth signals natively
• Without growth signals: You lose the third dimension of triangulation — prospect momentum and buying power indicators

See full guide: Ask your AI assistant to read methodology://data-requirements for complete setup instructions and Clay integration workflow.

Quick Start

Install via PyPI

pip install artefact-mcp

Install via Smithery

npx @smithery/cli install artefact-revenue-intelligence

Claude Code

claude mcp add artefact-revenue -- uvx artefact-mcp

Then ask:

• "What signals are you detecting in my pipeline?"
• "What's our dominant scaling constraint?"
• "Analyze the health of our Growth Engine"
• "Propose a GTM change: narrow ICP to SaaS companies with 50-200 employees"
• "Run an RFM analysis on our HubSpot data"
• "Qualify this prospect: SaaS company, $5M revenue, 80 employees in Ontario"
• "Score our pipeline health with exit criteria testing"

Claude Desktop

Add to claude_desktop_config.json:

Recommended (Python method):

{
  "mcpServers": {
    "artefact-revenue": {
      "command": "python3",
      "args": ["-m", "artefact_mcp"],
      "env": {
        "HUBSPOT_API_KEY": "pat-na1-xxxxxxxx"
      }
    }
  }
}

Alternative (uvx method):

{
  "mcpServers": {
    "artefact-revenue": {
      "command": "uvx",
      "args": ["artefact-mcp"],
      "env": {
        "HUBSPOT_API_KEY": "pat-na1-xxxxxxxx"
      }
    }
  }
}

Note: If using uvx and seeing "Server disconnected" errors, see the Troubleshooting section below.

Cursor

Add to .cursor/mcp.json:

Recommended (Python method):

{
  "mcpServers": {
    "artefact-revenue": {
      "command": "python3",
      "args": ["-m", "artefact_mcp"],
      "env": {
        "HUBSPOT_API_KEY": "pat-na1-xxxxxxxx"
      }
    }
  }
}

Alternative (uvx method):

{
  "mcpServers": {
    "artefact-revenue": {
      "command": "uvx",
      "args": ["artefact-mcp"],
      "env": {
        "HUBSPOT_API_KEY": "pat-na1-xxxxxxxx"
      }
    }
  }
}

Programmatic (Python)

from artefact_mcp.tools.signals import detect_signals
from artefact_mcp.tools.constraints import identify_dominant_constraint
from artefact_mcp.tools.engines import analyze_engine
from artefact_mcp.tools.gtm_commits import propose_gtm_change
from artefact_mcp.tools.rfm import run_rfm_analysis
from artefact_mcp.tools.icp import qualify_prospect
from artefact_mcp.tools.pipeline import score_pipeline

# Signal detection (no HubSpot key needed)
signals = detect_signals(source="sample")

# Dominant constraint analysis
constraint = identify_dominant_constraint(source="sample", quota=500000)

# Value engine health
engine = analyze_engine(engine_type="growth", source="sample")

# GTM commit drafting
commit = propose_gtm_change(
    entity_type="icp",
    change_description="Narrow ICP to SaaS companies with 50-200 employees",
    signal_type="win_loss_pattern",
    signal_data={"win_rate_saas": 0.45, "win_rate_other": 0.22},
)

# RFM with sample data
results = run_rfm_analysis(source="sample", industry_preset="b2b_service")

# ICP qualification
score = qualify_pro

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*README truncated. [View full README on GitHub](https://github.com/alexboissAV/artefact-mcp-server).*