ICP Intelligence MCP Server (io.github.shashwatgtm/icp-intelligence-mcp)

The MCP server “io.github.shashwatgtm/icp-intelligence-mcp” provides deep ICP analysis with pattern detection. Its readme describes 9 B2B targeting tools supporting ideal customer profiling, market sizing, buyer mapping, and account prioritization.

🛠️ Key Features

Deep ICP analysis
Pattern detection
9 B2B targeting tools
Focus areas: ideal customer profiling, market sizing, buyer mapping, account prioritization
MIT-licensed package (per readme excerpt)

🚀 Use Cases

Ideal customer profiling for B2B
Market sizing
Buyer mapping
Account prioritization

⚡ Developer Benefits

Structured toolset for ICP-related workflows (9 tools)
Accessible via the MCP registry (badge shown in excerpt)
Distributed as an npm package (badge shown in excerpt)

⚠️ Limitations

Server description provided here does not enumerate the individual tools or their exact inputs/outputs.

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.

🚀 Quick Start

bash

# Run directly with npx
npx -y @shashwatgtmalpha/icp-intelligence-mcp

Claude Desktop Configuration

Add to your claude_desktop_config.json:

json

{
  "mcpServers": {
    "icp-intelligence-mcp": {
      "command": "npx",
      "args": ["-y", "@shashwatgtmalpha/icp-intelligence-mcp"]
    }
  }
}

🛠️ 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

👤 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
🎯 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

📖 Tool Details

1. ICP Deep Dive (`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.

2. ICP Scoring Model (`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.

3. ICP Gap Analysis (`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.

4. ICP Evolution Tracker (`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.

5. ICP Interview Synthesizer (`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.

6. Buyer Group Analyzer (`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.

7. TAM SAM SOM Calculator (`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.

8. Lookalike Signal Generator (`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.

9. Account Prioritization (`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

📚 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

👨‍💻 Author

Shashwat Ghosh - Founder, Helix GTM Consulting

📄 License

MIT License - see LICENSE for details.

Part of the GTM Helix MCP Suite - AI-powered B2B go-to-market tools

io.github.shashwatgtm/icp-intelligence-mcp

ICP Intelligence MCP Server (io.github.shashwatgtm/icp-intelligence-mcp)

🛠️ Key Features

🚀 Use Cases

⚡ Developer Benefits

⚠️ Limitations

ICP Intelligence MCP v1.0.0