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Methodology-driven MCP server for revenue intelligence: RFM analysis, 14.5-point ICP scoring, pipeline health scoring, and HubSpot integration

Project description

Artefact Revenue Intelligence MCP Server

PyPI MCP Compatible License: BSL-1.1

Methodology-driven revenue intelligence for AI assistants. Not a generic HubSpot CRUD wrapper.

A Model Context Protocol (MCP) server that gives AI agents access to battle-tested revenue intelligence tools — RFM analysis, 14.5-point ICP qualification, and pipeline health scoring. Built on the Artefact Formula methodology from real B2B consulting engagements.

Why Artefact MCP?

Feature HubSpot Official MCP Generic Wrappers Artefact MCP
CRUD operations Yes Yes Via HubSpot API
RFM Analysis No No 11-segment classification
ICP Scoring No No 14.5-point model
Pipeline Health No No 0-100 health score
Methodology built-in No No Artefact Formula
Works without API key No No Yes (demo data)

Who Is This For?

  • B2B revenue teams using HubSpot who want AI-powered customer segmentation
  • RevOps managers who need pipeline health analysis accessible from Claude or Cursor
  • Consultants who deliver RFM analysis and ICP scoring to clients
  • Developers building revenue intelligence integrations with MCP

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. Supports B2B service, SaaS, and manufacturing presets.

qualify — ICP Qualification (14.5-Point Model)

Scores a prospect across three dimensions:

  • Firmographic Fit (5 pts): Industry, revenue, employees, geography
  • Behavioral Fit (5 pts): Tech stack, growth signals, engagement, purchase history
  • Strategic Fit (4.5 pts): Decision-maker access, budget authority, alignment

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. Returns a 0-100 health score.

Resources

URI Description
methodology://scoring-model 14.5-point ICP scoring model reference
methodology://tier-definitions 4-tier classification system
methodology://rfm-segments 11 RFM segment definitions with scoring scales
methodology://spiced-framework SPICED discovery framework

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:

  • "Run an RFM analysis on our HubSpot data"
  • "Qualify this prospect: SaaS company, $5M revenue, 80 employees in Ontario"
  • "Score our pipeline health"

Claude Desktop

Add to claude_desktop_config.json:

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

Cursor

Add to .cursor/mcp.json:

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

Programmatic (Python)

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

# RFM with sample data (no HubSpot key needed)
results = run_rfm_analysis(source="sample", industry_preset="b2b_service")

# ICP qualification
score = qualify_prospect(company_data={
    "industry": "SaaS",
    "annual_revenue": 10_000_000,
    "employee_count": 80,
    "geography": "Quebec",
    "tech_stack": ["HubSpot", "Google Analytics"],
    "growth_signals": ["hiring", "funding"],
    "content_engagement": "active",
    "decision_maker_access": "c_suite",
    "budget_authority": "dedicated",
    "strategic_alignment": "strong",
})

# Pipeline health
health = score_pipeline(source="sample")

Configuration

Variable Required Description
HUBSPOT_API_KEY No HubSpot private app token. Without it, tools work with source="sample".
ARTEFACT_LICENSE_KEY No License key for Pro/Enterprise tier. Free tier (sample data) works without a key.

Pricing

Tier Price What You Get
Free $0 All 3 tools with built-in demo data (source="sample")
Pro $149/mo Live HubSpot integration + all methodology resources
Enterprise $499/mo Pro + priority support + custom scoring presets

Purchase a license

Alternatives & Comparisons

  • HubSpot Official MCP Server — Read-only CRUD access to CRM objects. No scoring or intelligence.
  • CData HubSpot MCP — SQL-based access to HubSpot data. No built-in methodology.
  • Zapier MCP — Action triggers and workflow automation. Different use case.
  • Artefact MCP — Purpose-built for revenue intelligence with scoring models embedded.

FAQ

Q: What MCP server should I use for revenue intelligence? A: Artefact MCP is the only MCP server with built-in RFM analysis, ICP qualification scoring (14.5-point model), and pipeline health analysis specifically designed for B2B revenue teams.

Q: Does this replace the official HubSpot MCP server? A: They serve different purposes. HubSpot's server provides CRUD access to CRM objects. Artefact MCP provides intelligence and scoring on top of that data.

Q: Can I use this without a HubSpot API key? A: Yes. All tools work with built-in demo data using source="sample".

Q: What data does this send externally? A: Tool results stay local. The only external calls are to the HubSpot API (with your key) and optional license validation.

Development

git clone https://github.com/alexboissAV/artefact-mcp-server.git
cd artefact-mcp-server
pip install -e ".[dev]"
pytest tests/

Dependencies

  • fastmcp>=2.0 — MCP server framework
  • httpx>=0.25.0 — HTTP client for HubSpot API

No pandas, numpy, or heavy data libraries. Pure Python scoring logic.

License

Business Source License 1.1 — Free to use for connecting to MCP tools via AI assistants. Scoring methodology may not be extracted for competing products. Converts to MIT in 2030.

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