io.github.shashwatgtm/icp-intelligence-mcp
Deep ICP Analysis with Pattern Detection - 9 B2B targeting tools
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ICP Intelligence MCP v1.0.0
Deep ICP Analysis with Pattern Detection - 9 tools for ideal customer profiling, market sizing, buyer mapping, and account prioritization.
π Quick Start
# Run directly with npx
npx -y @shashwatgtmalpha/icp-intelligence-mcp
Claude Desktop Configuration
Add to your claude_desktop_config.json:
{
"mcpServers": {
"icp-intelligence-mcp": {
"command": "npx",
"args": ["-y", "@shashwatgtmalpha/icp-intelligence-mcp"]
}
}
}
π οΈ Tools Overview
| 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 |
π€ Who Is This For?
Primary Users
| 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-Tool Mapping
| 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 |
Recommended Agent Skills
This MCP is included in these user-focused Agent bundles:
| Agent Bundle | Tools Count | Best For |
|---|---|---|
| π― Founder GTM Copilot | 10 tools | Founders, early-stage CEOs |
| π SDR Toolkit | 8 tools | SDRs, BDRs |
| π― Product Marketing Engine | 12 tools | PMMs |
| π Demand Gen & Ops | 10 tools | Demand gen, marketing ops |
| πΌ Account Executive Deal Desk | 12 tools | AEs, account managers |
π Tool Details
1. ICP Deep Dive (icp_deep_dive)
Detect patterns from customer data to define ICP attributes.
Inputs:
| Parameter | Required | Description |
|---|---|---|
customer_data | β | Description of current customers |
best_customers | β | Characteristics of top customers |
industry_focus | β | Industry context |
Output: ICP profile with firmographics, technographics, behavioral signals, and champion characteristics.
2. ICP Scoring Model (icp_scoring_model)
Generate auto-weighted qualification scorecards.
Inputs:
| Parameter | Required | Description |
|---|---|---|
icp_attributes | β | Key ICP characteristics |
deal_data | β | Win/loss data for weighting |
scoring_type | β | lead, account, opportunity |
Output: Weighted scorecard with tiers, thresholds, and implementation guidance.
3. ICP Gap Analysis (icp_gap_analysis)
Compare current customers to ideal profile.
Inputs:
| Parameter | Required | Description |
|---|---|---|
current_customers | β | Current customer characteristics |
ideal_icp | β | Target ICP definition |
key_metrics | β | Metrics to compare (ACV, retention, etc.) |
Output: Gap matrix, metric comparison, recommendations for ICP refinement.
4. ICP Evolution Tracker (icp_evolution_tracker)
Monitor ICP changes over time.
Inputs:
| Parameter | Required | Description |
|---|---|---|
historical_data | β | Past customer/deal data |
time_period | β | Analysis timeframe |
win_loss_patterns | β | Recent win/loss trends |
Output: ICP drift analysis, emerging segments, recommended adjustments.
5. ICP Interview Synthesizer (icp_interview_synthesizer)
Extract patterns from customer interviews.
Inputs:
| Parameter | Required | Description |
|---|---|---|
interview_notes | β | Interview transcripts or notes |
interview_type | β | discovery, win, loss, churn |
focus_areas | β | Specific areas to analyze |
Output: Pattern themes, quotes, ICP refinement recommendations.
6. Buyer Group Analyzer (buyer_group_analyzer)
Map buying committee decision dynamics.
Inputs:
| Parameter | Required | Description |
|---|---|---|
product | β | Your product/service |
target_company_size | β | SMB, mid-market, enterprise |
deal_complexity | β | simple, moderate, complex |
Output: Committee map (champion, economic, technical, user, blocker) with engagement strategies.
7. TAM SAM SOM Calculator (tam_sam_som_calculator)
Bottom-up market sizing with deal targets.
Inputs:
| Parameter | Required | Description |
|---|---|---|
product | β | Your product/service |
target_segments | β | Market segments |
pricing | β | Price point or ACV |
geographic_focus | β | Target geography |
data_sources | β | Available market data |
Output: TAM/SAM/SOM with methodology, assumptions, and quarterly deal targets.
8. Lookalike Signal Generator (lookalike_signal_generator)
Generate platform-specific targeting criteria.
Inputs:
| Parameter | Required | Description |
|---|---|---|
icp_profile | β | ICP characteristics |
platforms | β | linkedin, google_ads, 6sense, zoominfo, etc. |
budget_tier | β | low, medium, high |
Output: Platform-specific targeting fields, audience sizes, recommended exclusions.
9. Account Prioritization (account_prioritization)
Multi-dimensional account ranking.
Inputs:
| Parameter | Required | Description |
|---|---|---|
accounts | β | List of accounts to prioritize |
icp_criteria | β | Scoring criteria |
intent_signals | β | Available intent data |
relationship_data | β | Existing relationships |
Output: Tiered account list (Tier 1/2/3) with scoring rationale and engagement recommendations.
π Related MCPs
| 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 |
π ICP Intelligence Philosophy
This MCP is built on the principle that ICP is dynamic, not static. The best B2B companies continuously refine their ICP based on:
- Win/loss patterns
- Customer success metrics
- Market evolution
- Product capabilities
Key Principles:
- Data-driven: Ground ICP in actual customer data
- Multi-dimensional: Beyond firmographics to behavior
- Actionable: Translate ICP to targeting criteria
- Iterative: Regular refinement cycles
π¨βπ» Author
Shashwat Ghosh - Founder, Helix GTM Consulting
π License
MIT License - see LICENSE for details.
Part of the GTM Helix MCP Suite - AI-powered B2B go-to-market tools
