Artefact Revenue Intelligence MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tasks | {
"list": {},
"cancel": {},
"requests": {
"tools": {
"call": {}
},
"prompts": {
"get": {}
},
"resources": {
"read": {}
}
}
} |
| tools | {
"listChanged": true
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| run_rfmA | Run RFM (Recency, Frequency, Monetary) analysis on client data. Scores clients based on purchase behavior, segments them into 11 categories, and extracts ICP patterns from top performers. Args: source: Data source — "auto" (uses HubSpot if API key is set, otherwise sample data), "hubspot" for live HubSpot data, "sample" for built-in demo data. industry_preset: Scoring preset — "b2b_service", "saas", "manufacturing", or "default". Returns: JSON with scored clients, segment distribution, ICP patterns, and tier recommendations. |
| qualifyA | Score a prospect against the Artefact 14.5-point ICP model. Evaluates Firmographic Fit (5 pts), Behavioral Fit (5 pts), and Strategic Fit (4.5 pts). Returns tier classification (1-4), score breakdown, and recommended engagement strategy. Provide EITHER company_id (HubSpot ID, requires HUBSPOT_API_KEY) OR company_data (JSON string). Args: company_id: HubSpot company ID to fetch and score. company_data: JSON string with company attributes. Example keys: industry, annual_revenue, employee_count, geography, tech_stack (list), growth_signals (list), content_engagement ("active"|"occasional"|"none"), purchase_history ("regular"|"occasional"|"never"), decision_maker_access ("c_suite"|"director"|"manager"|"indirect"|"none"), budget_authority ("dedicated"|"shared"|"possible"|"none"), strategic_alignment ("strong"|"partial"|"misaligned"). scoring_config: Optional JSON string to override default scoring parameters. Customize the model for your business. Example keys: primary_industries (list), adjacent_industries (list), excluded_industries (list), revenue_range ([min, max]), employee_range ([min, max]), primary_geography (list), secondary_geography (list). Returns: JSON with total score, tier, breakdown, exclusion check, and recommended action. |
| score_pipeline_healthA | Analyze pipeline health with velocity metrics, conversion rates, and at-risk detection. Calculates overall health score (0-100), identifies bottleneck stages, measures stage-to-stage conversion rates, and flags stalled or overdue deals. Args: pipeline_id: Optional HubSpot pipeline ID to filter. Default: all pipelines. source: "auto" (uses HubSpot if API key is set, otherwise sample data), "hubspot" for live data, "sample" for built-in demo data. Returns: JSON with health score, velocity metrics, conversion rates, at-risk deals, and stage distribution. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| scoring_model | ICP 14.5-point scoring model reference. |
| tier_definitions | 4-tier classification system (Ideal / Strong / Moderate / Poor). |
| rfm_segments | 11 RFM segment definitions with scoring scales. |
| spiced_framework | SPICED discovery framework reference. |
| server_version | Server version and status information. |
TDQS
Scored across 3 tools
Each tool targets a distinct aspect of revenue intelligence: prospect qualification, customer RFM analysis, and pipeline health. No overlap in functionality.
All tools use lowercase, underscore-separated verb-object patterns (qualify, run_rfm, score_pipeline_health). Consistent and predictable.
Three tools cover the core domains of revenue intelligence, but the scope is broad enough that additional tools (e.g., for model management or forecasting) could be expected.
Covers prospect scoring, customer segmentation, and pipeline health. Minor gaps include lack of tools for historical trend analysis or custom model configuration without input parameters.