Icp Intelligence MCP

🚀 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

# 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"]
    }
  }
}

✨ Features

This ICP Intelligence MCP provides 9 powerful tools for ideal customer profiling, market sizing, buyer mapping, and account prioritization, enabling businesses to better understand their customers and markets.

📦 Installation

Direct Run with npx

npx -y @shashwatgtmalpha/icp-intelligence-mcp

Claude Desktop Configuration

Add the following configuration to your claude_desktop_config.json:

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

💻 Usage Examples

Basic Usage

npx -y @shashwatgtmalpha/icp-intelligence-mcp

📚 Documentation

Tools Overview

Who Is This For?

Primary Users

Job-to-Tool Mapping

Recommended Agent Skills

This MCP is included in these user-focused Agent bundles:

Tool Details

1. ICP Deep Dive (icp_deep_dive)

Detect patterns from customer data to define ICP attributes.

Inputs:

Output: ICP profile with firmographics, technographics, behavioral signals, and champion characteristics.

2. ICP Scoring Model (icp_scoring_model)

Generate auto-weighted qualification scorecards.

Inputs:

Output: Weighted scorecard with tiers, thresholds, and implementation guidance.

3. ICP Gap Analysis (icp_gap_analysis)

Compare current customers to ideal profile.

Inputs:

Output: Gap matrix, metric comparison, recommendations for ICP refinement.

4. ICP Evolution Tracker (icp_evolution_tracker)

Monitor ICP changes over time.

Inputs:

Output: ICP drift analysis, emerging segments, recommended adjustments.

5. ICP Interview Synthesizer (icp_interview_synthesizer)

Extract patterns from customer interviews.

Inputs:

Output: Pattern themes, quotes, ICP refinement recommendations.

6. Buyer Group Analyzer (buyer_group_analyzer)

Map buying committee decision dynamics.

Inputs:

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:

Output: TAM/SAM/SOM with methodology, assumptions, and quarterly deal targets.

8. Lookalike Signal Generator (lookalike_signal_generator)

Generate platform-specific targeting criteria.

Inputs:

Output: Platform-specific targeting fields, audience sizes, recommended exclusions.

9. Account Prioritization (account_prioritization)

Multi-dimensional account ranking.

Inputs:

Output: Tiered account list (Tier 1/2/3) with scoring rationale and engagement recommendations.

Related MCPs

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