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_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 with exit criteria
health = score_pipeline(source="sample", exit_criteria=[
    {"stage": "Discovery", "criterion": "SPICED complete", "required_proof": "All 6 SPICED fields populated"}
])

Troubleshooting

Server Disconnected Errors (uvx PATH issue)

Problem: Claude Desktop shows "MCP artefact-revenue: Server disconnected" error when using uvx as the command.

Cause: Claude Desktop (and other sandboxed applications) may not have access to uvx in your PATH. This commonly happens when uvx is installed via:

• Homebrew → ~/.local/bin/uvx
• curl installation → ~/.cargo/bin/uvx or other locations

Solutions:

1. Use Python method (recommended): Switch to python3 -m artefact_mcp method (see Claude Desktop section above). Python is always in PATH.

2. Use full uvx path: Find your uvx location and use the full path:

   # Find uvx location
   which uvx
   # Example output: /Users/yourname/.local/bin/uvx
   

   Then update your config with the full path:

   {
     "mcpServers": {
       "artefact-revenue": {
         "command": "/Users/yourname/.local/bin/uvx",
         "args": ["artefact-mcp"],
         "env": {}
       }
     }
   }
   

3. Verify manually: Test that the MCP server starts correctly:

   uvx artefact-mcp==0.3.3
   # Should see: "Artefact Revenue Intelligence MCP Server running..."
   

Other Issues

Issue: Tools return "No HubSpot API key" errors.

Solution: Ensure HUBSPOT_API_KEY is set in your MCP server configuration. Or use source="sample" to test with demo data first.

Issue: Import errors when using python3 -m artefact_mcp.

Solution: Ensure the package is installed: pip install artefact-mcp or pip install --upgrade artefact-mcp.

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.
ARTEFACT_PROPERTY_MAPPING_PATH	No	Path to JSON file with custom HubSpot property mappings (Pro/Enterprise only).
ARTEFACT_RFM_THRESHOLDS_PATH	No	Path to JSON file with custom RFM scoring thresholds (Pro/Enterprise only).

Custom Property Mappings (Pro/Enterprise)

If your HubSpot instance uses custom property names for behavioral and strategic fit data, you can configure property mappings. This allows the qualify tool to automatically fetch and score all ICP dimensions from your HubSpot data.

Create a JSON configuration file (e.g., artefact_property_mapping.json):

{
  "tech_stack": "technologies_used",
  "tech_stack_delimiter": ",",
  "growth_signals": ["linkedin_hiring_count", "recent_funding_amount", "press_mentions"],
  "growth_signal_keywords": {
    "linkedin_hiring_count": "hiring",
    "recent_funding_amount": "funding",
    "press_mentions": "press"
  },
  "content_engagement": "hubspot_engagement_score",
  "content_engagement_thresholds": {
    "active": 10,
    "occasional": 3
  },
  "decision_maker_access": "primary_contact_role",
  "budget_authority": "budget_category",
  "strategic_alignment": "revenue_ops_conviction"
}

Set the environment variable:

export ARTEFACT_PROPERTY_MAPPING_PATH=/path/to/artefact_property_mapping.json

Available Configuration Options:

Property	Type	Description	Default
tech_stack	string	HubSpot property name for tech stack	None
tech_stack_delimiter	string	Delimiter for parsing text fields	";"
growth_signals	array	List of HubSpot properties indicating growth	None
growth_signal_keywords	object	Map property names to signal keywords	{}
content_engagement	string	HubSpot property for engagement score	None
content_engagement_thresholds	object	Thresholds for active/occasional	{"active": 5, "occasional": 1}
decision_maker_access	string	Strategic fit property	None
budget_authority	string	Budget authority property	None
strategic_alignment	string	Strategic alignment property	None

Example HubSpot Properties:

Common custom properties to map:

• Tech Stack: tech_stack_used, technologies, crm_platform
• Growth Signals: linkedin_job_postings_count, recent_funding_round, press_mentions_count, new_office_opened
• Content Engagement: hs_analytics_num_page_views, email_engagement_score
• Strategic Fit: primary_contact_role, budget_category, growth_conviction

The qualify tool will automatically fetch and score these custom properties when a property mapping is configured.

Example Configuration Files:

Two example configurations are included in the repository:

• property_mapping.example.json — Full configuration with all available options
• property_mapping.minimal.example.json — Minimal configuration for growth signals only

Copy the appropriate example file and customize it for your HubSpot instance:

cp property_mapping.minimal.example.json my_property_mapping.json
# Edit my_property_mapping.json with your HubSpot property names
export ARTEFACT_PROPERTY_MAPPING_PATH=$(pwd)/my_property_mapping.json

Custom RFM Thresholds (Pro/Enterprise)

Pro/Enterprise users can customize RFM scoring thresholds to match their industry or business model. The built-in presets (b2b_service, saas, manufacturing) may not perfectly fit your buying cycles or revenue ranges.

Create an RFM threshold configuration file (e.g., rfm_thresholds.json):

{
  "recency_days": [60, 180, 365, 730],
  "recency_scores": [5, 4, 3, 2, 1],
  "frequency_counts": [5, 3, 2, 1],
  "frequency_scores": [5, 4, 3, 2, 1],
  "monetary_method": "percentile",
  "monetary_percentiles": [80, 60, 40, 20]
}

Set the environment variable:

export ARTEFACT_RFM_THRESHOLDS_PATH=/path/to/rfm_thresholds.json

Available Configuration Options:

Property	Type	Description	Default
recency_days	array	Days since last purchase thresholds	[30, 90, 180, 365]
recency_scores	array	Scores for each recency band (5 = best)	[5, 4, 3, 2, 1]
frequency_counts	array	Transaction count thresholds	[10, 5, 3, 2]
frequency_scores	array	Scores for each frequency band	[5, 4, 3, 2, 1]
monetary_method	string	Scoring method: "percentile" or "fixed"	"percentile"
monetary_percentiles	array	Percentile thresholds (for percentile method)	[80, 60, 40, 20]
monetary_fixed_thresholds	array	Fixed dollar thresholds (for fixed method)	[100000, 50000, 25000, 10000]
monetary_scores	array	Scores for each monetary band	[5, 4, 3, 2, 1]

Example Configurations:

• rfm_thresholds.example.json — Percentile-based monetary scoring (recommended for most use cases)
• rfm_thresholds.fixed_monetary.example.json — Fixed dollar thresholds for monetary scoring

When to Use Fixed Thresholds:

Use "monetary_method": "fixed" when:

• You have specific revenue tiers that define customer value (e.g., $100K+ = enterprise)
• Your customer base has wide revenue variance and percentiles don't align with business value
• You want consistent scoring across different time periods

Use "monetary_method": "percentile" (default) when:

• You want relative scoring within your current customer base
• Your customer base is relatively homogeneous
• You want the top 20% of customers to always score 5, regardless of absolute revenue

Custom Configuration Example:

cp rfm_thresholds.example.json my_rfm_thresholds.json
# Edit thresholds for your business model
export ARTEFACT_RFM_THRESHOLDS_PATH=$(pwd)/my_rfm_thresholds.json

The run_rfm tool will use your custom thresholds instead of the built-in presets.

## Pricing

| Tier | Price | What You Get |
|------|-------|-------------|
| **Free** | $0 | All 7 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](https://artefactventures.lemonsqueezy.com)

## 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 that treats GTM like a codebase — with signal detection, constraint analysis, value engine health, and structured GTM commit proposals. Plus ICP Triangulation, RFM analysis, and pipeline health scoring 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

```bash
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.

Star History

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.