This is a B2B marketing toolkit that surfaces nine customer profiling operations through MCP. You get pattern detection from customer data (icp_deep_dive), lead scoring model generation (icp_scoring_model), gap analysis between current and ideal customers, evolution tracking for ICP drift, interview synthesis for voice of customer patterns, buying committee mapping, TAM/SAM/SOM calculations, lookalike audience generation for ad platforms like LinkedIn and Google Ads, and multi-dimensional account prioritization. It's built for the workflow where you're constantly refining who to target and how to score them. Operations take structured inputs like customer data or interview notes and return weighted scorecards, targeting criteria, or tiered account lists. Aimed at founders doing market sizing, demand gen teams building targeting, and sales ops creating qualification models.
Deep ICP Analysis with Pattern Detection - 9 tools for ideal customer profiling, market sizing, buyer mapping, and account prioritization.
# Run directly with npx
npx -y @shashwatgtmalpha/icp-intelligence-mcp
Add to your claude_desktop_config.json:
{
"mcpServers": {
"icp-intelligence-mcp": {
"command": "npx",
"args": ["-y", "@shashwatgtmalpha/icp-intelligence-mcp"]
}
}
}
| Tool | Purpose | Primary Output |
|---|---|---|
icp_deep_dive | Pattern detection from customer data | ICP profile with attributes |
icp_scoring_model | Auto-weighted qualification scorecards | Lead/account scoring model |
icp_gap_analysis | Current vs ideal customer comparison | Metric gaps & recommendations |
icp_evolution_tracker | Dynamic ICP monitoring | Win/loss pattern trends |
icp_interview_synthesizer | Extract patterns from interviews | Voice of customer insights |
buyer_group_analyzer | Decision dynamics mapping | Buying committee profiles |
tam_sam_som_calculator | Bottom-up market sizing | Market size with deal targets |
lookalike_signal_generator | Platform-specific targeting | Ad platform targeting criteria |
account_prioritization | Multi-dimensional ranking | Prioritized account tiers |
| Role | Key Tools | Use Cases |
|---|---|---|
| Founders/CEOs | tam_sam_som_calculator, icp_deep_dive | Market sizing, customer definition |
| CMOs/VPs Marketing | icp_gap_analysis, icp_evolution_tracker | ICP health monitoring |
| Product Marketing | buyer_group_analyzer, icp_interview_synthesizer | Buying committee, VOC |
| Demand Gen | lookalike_signal_generator, account_prioritization | Targeting, ABM |
| Sales Ops/RevOps | icp_scoring_model, account_prioritization | Lead scoring, account tiering |
| SDRs/BDRs | account_prioritization, icp_scoring_model | Account qualification |
| Job To Be Done | Recommended Tool |
|---|---|
| "I need to define our ideal customer profile" | icp_deep_dive |
| "I need to create a lead scoring model" | icp_scoring_model |
| "I need to compare our actual vs ideal customers" | icp_gap_analysis |
| "I need to track how our ICP is changing" | icp_evolution_tracker |
| "I need to synthesize customer interview insights" | icp_interview_synthesizer |
| "I need to map the buying committee" | buyer_group_analyzer |
| "I need to calculate our TAM/SAM/SOM" | tam_sam_som_calculator |
| "I need targeting criteria for ad platforms" | lookalike_signal_generator |
| "I need to prioritize our target accounts" | account_prioritization |
This MCP is included in these user-focused Agent bundles:
| Agent Bundle | Tools Count | Best For |
|---|---|---|
| 🎯 Founder GTM Copilot | 10 tools | Founders, early-stage CEOs |
| 📞 SDR Toolkit | 8 tools | SDRs, BDRs |
| 🎯 Product Marketing Engine | 12 tools | PMMs |
| 📊 Demand Gen & Ops | 10 tools | Demand gen, marketing ops |
| 💼 Account Executive Deal Desk | 12 tools | AEs, account managers |
icp_deep_dive)Detect patterns from customer data to define ICP attributes.
Inputs:
| Parameter | Required | Description |
|---|---|---|
customer_data | ✅ | Description of current customers |
best_customers | ❌ | Characteristics of top customers |
industry_focus | ❌ | Industry context |
Output: ICP profile with firmographics, technographics, behavioral signals, and champion characteristics.
icp_scoring_model)Generate auto-weighted qualification scorecards.
Inputs:
| Parameter | Required | Description |
|---|---|---|
icp_attributes | ✅ | Key ICP characteristics |
deal_data | ❌ | Win/loss data for weighting |
scoring_type | ❌ | lead, account, opportunity |
Output: Weighted scorecard with tiers, thresholds, and implementation guidance.
icp_gap_analysis)Compare current customers to ideal profile.
Inputs:
| Parameter | Required | Description |
|---|---|---|
current_customers | ✅ | Current customer characteristics |
ideal_icp | ✅ | Target ICP definition |
key_metrics | ❌ | Metrics to compare (ACV, retention, etc.) |
Output: Gap matrix, metric comparison, recommendations for ICP refinement.
icp_evolution_tracker)Monitor ICP changes over time.
Inputs:
| Parameter | Required | Description |
|---|---|---|
historical_data | ✅ | Past customer/deal data |
time_period | ❌ | Analysis timeframe |
win_loss_patterns | ❌ | Recent win/loss trends |
Output: ICP drift analysis, emerging segments, recommended adjustments.
icp_interview_synthesizer)Extract patterns from customer interviews.
Inputs:
| Parameter | Required | Description |
|---|---|---|
interview_notes | ✅ | Interview transcripts or notes |
interview_type | ❌ | discovery, win, loss, churn |
focus_areas | ❌ | Specific areas to analyze |
Output: Pattern themes, quotes, ICP refinement recommendations.
buyer_group_analyzer)Map buying committee decision dynamics.
Inputs:
| Parameter | Required | Description |
|---|---|---|
product | ✅ | Your product/service |
target_company_size | ✅ | SMB, mid-market, enterprise |
deal_complexity | ❌ | simple, moderate, complex |
Output: Committee map (champion, economic, technical, user, blocker) with engagement strategies.
tam_sam_som_calculator)Bottom-up market sizing with deal targets.
Inputs:
| Parameter | Required | Description |
|---|---|---|
product | ✅ | Your product/service |
target_segments | ✅ | Market segments |
pricing | ✅ | Price point or ACV |
geographic_focus | ❌ | Target geography |
data_sources | ❌ | Available market data |
Output: TAM/SAM/SOM with methodology, assumptions, and quarterly deal targets.
lookalike_signal_generator)Generate platform-specific targeting criteria.
Inputs:
| Parameter | Required | Description |
|---|---|---|
icp_profile | ✅ | ICP characteristics |
platforms | ✅ | linkedin, google_ads, 6sense, zoominfo, etc. |
budget_tier | ❌ | low, medium, high |
Output: Platform-specific targeting fields, audience sizes, recommended exclusions.
account_prioritization)Multi-dimensional account ranking.
Inputs:
| Parameter | Required | Description |
|---|---|---|
accounts | ✅ | List of accounts to prioritize |
icp_criteria | ✅ | Scoring criteria |
intent_signals | ❌ | Available intent data |
relationship_data | ❌ | Existing relationships |
Output: Tiered account list (Tier 1/2/3) with scoring rationale and engagement recommendations.
| MCP | Focus | Tools | Link |
|---|---|---|---|
| CRAFT GTM | GTM strategy | 8 | GitHub |
| CRAFT Content | Content creation | 8 | GitHub |
| IMPACT | B2B positioning | 8 | GitHub |
| Revenue Enablement | Sales execution | 12 | GitHub |
This MCP is built on the principle that ICP is dynamic, not static. The best B2B companies continuously refine their ICP based on:
Key Principles:
Shashwat Ghosh - Founder, Helix GTM Consulting
MIT License - see LICENSE for details.
Part of the GTM Helix MCP Suite - AI-powered B2B go-to-market tools