Clayton Christensen persona agent for strategic advisory using disruption theory
{
"mcpServers": {
"io-github-m2ai-mcp-servers-mcp-christensen": {
"command": "<see-readme>",
"args": []
}
}
}No install config available. Check the server's README for setup instructions.
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Clayton Christensen persona agent for strategic advisory using disruption theory
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Last commit 79 days ago. 1 stars.
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Transport: stdio. Works with Claude Desktop, Cursor, Claude Code, and most MCP clients.
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A Clayton Christensen persona agent for strategic advisory, implemented as an MCP (Model Context Protocol) server.
Apply disruption theory, jobs-to-be-done, and capabilities analysis to your business decisions.
This MCP server gives Claude the ability to analyze decisions through Clayton Christensen's frameworks:
When you connect this server to Claude Desktop or Claude Code, you get a strategic advisor that thinks like Christensen—asking the right questions, connecting to historical patterns, and offering theory-grounded insights with appropriate humility.
# Clone the repository
git clone https://github.com/m2ai-mcp-servers/mcp-christensen.git
cd mcp-christensen
# Install dependencies
npm install
# Build
npm run build
Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on Mac or %APPDATA%\Claude\claude_desktop_config.json on Windows):
{
"mcpServers": {
"christensen": {
"command": "node",
"args": ["/path/to/christensen-mcp/dist/index.js"]
}
}
}
Add to your project's .mcp.json:
{
"mcpServers": {
"christensen": {
"command": "node",
"args": ["./path/to/christensen-mcp/dist/index.js"]
}
}
}
analyze_decisionFull strategic analysis using all Christensen frameworks.
Use the analyze_decision tool to evaluate: Should we pursue enterprise customers or focus on SMBs?
Parameters:
decision (required): The business decision to analyzecontext: Additional context about your situationorganization: Your org type, size, industry, current focusmarket: Incumbents, competitors, customer segmentsfocusAreas: Specific frameworks to apply (jtbd, disruption, cpp, resource-dependence)case_studyExplore Christensen's canonical case studies and match them to your situation.
Use the case_study tool to find patterns matching: We're a low-cost entrant being ignored by incumbents
Available cases:
steel_minimills: Low-end disruption with asymmetric motivationdisk_drives: New-market disruption with different value networkmilkshake: Jobs-to-be-done discovery through circumstancehonda_motorcycles: Emergent strategy through market learningintel_microprocessors: Capability migration through process investmentget_frameworkLearn about any Christensen framework in detail.
Use the get_framework tool with framework: jtbd and detail: full
Frameworks: jtbd, disruption, cpp, resource-dependence, all
Detail levels: summary, full, questions-only
You: I'm trying to decide whether to build individual AI agents or invest in a factory process for creating them. Can you analyze this using Christensen's frameworks?
Claude: Uses analyze_decision tool
Let me think through this using Christensen's frameworks...
Jobs-to-Be-Done: What job are your customers hiring an AI agent to do? If they need specialized expertise (functional), confidence in the advice (emotional), and credibility (social), individual high-quality agents might serve that job better. But if they need to create their own agents quickly, the factory serves a different job entirely.
The Intel Pattern: I'm reminded of Intel's pivot from memory to microprocessors. The insight wasn't about resources—Japanese firms could acquire those. It was about processes. Intel's chip design an