Mikensey MCP Server
Provides McKinsey-style strategy intelligence for real estate by enabling AI assistants to search podcast transcripts, access industry benchmarks, and apply consulting frameworks like 2×2 matrices and issue trees to analyze business situations.
README
Mikensey MCP Server
McKinsey-style strategy intelligence for real estate — powered by 53 episodes of Mike DelPrete's Context podcast.
An open-source MCP server that gives Claude (or any MCP-compatible AI) access to real estate industry benchmarks, consulting frameworks, and full-text transcript search. Ask it to analyze your business situation using 2×2 matrices, SCR memos, issue trees, or Porter's Five Forces — grounded in real data from industry leaders.
Tools
| Tool | What it does |
|---|---|
mikensey_search |
Full-text search across all 53 transcripts |
mikensey_list_episodes |
Browse and filter episodes by guest or topic |
mikensey_get_episode |
Read full or partial transcripts |
mikensey_get_benchmarks |
Query 26 industry benchmarks (attach rates, costs, growth metrics) |
mikensey_get_frameworks |
Browse 10 industry frameworks + 6 consulting frameworks |
mikensey_analyze |
Apply a consulting framework (2×2, SCR, Issue Tree, Porter's, Value Chain, MECE) to your situation |
mikensey_get_advice |
SCR-structured strategy brief combining search, benchmarks, and frameworks |
Quick Start
git clone https://github.com/YOUR_USERNAME/mikensey-mcp-server.git
cd mikensey-mcp-server
npm install
npm run build
Add to Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json on Mac):
{
"mcpServers": {
"mikensey": {
"command": "node",
"args": ["/path/to/mikensey-mcp-server/dist/index.js"],
"env": {
"MIKENSEY_TRANSCRIPT_DIR": "/path/to/your/transcript-files"
}
}
}
}
Restart Claude Desktop. You'll see "mikensey" in the tools menu.
Usage
Just talk to Claude naturally:
- "Analyze my situation using a 2×2 matrix — I'm a broker losing agents to cloud brokerages"
- "Give me an SCR strategy memo — I'm a mortgage LO worried about AI replacing my job"
- "Build an issue tree for how a proptech startup can achieve profitability"
- "What are the benchmark attach rates for mortgage in real estate?"
- "Which episodes cover Zillow's strategy?"
What's Inside
26 industry benchmarks — specific numbers cited by leaders like Tamir Poleg (Real), Glenn Sanford (eXp), Garth Graham (Stratmore), Justin Messer (Prosperity), and more. Attach rates, origination costs, agent productivity, growth metrics.
10 industry frameworks — mental models from the podcast: the 3-Lever Consumer Value Model, the Brokerage Pretzel, the Agent Value Equation, Hand-to-Hand Combat, W-H-Y sales framework, and more.
6 consulting frameworks — McKinsey-style analytical tools: 2×2 Matrix, Situation-Complication-Resolution (SCR), Issue Trees (MECE), Porter's Five Forces, Value Chain Analysis, and MECE Breakdowns. Each comes with real estate examples and pre-built templates.
4 pre-built 2×2 matrices — Brokerage Business Models, Mortgage Origination Strategy, Proptech Startup Survival, Agent Recruitment & Retention — with companies plotted and insights synthesized from transcript evidence.
3 pre-built issue trees — Brokerage Profitability, Mortgage Growth Strategy, Proptech Product-Market Fit — with MECE branches and supporting data.
Transcript Format
The server expects .txt files named like:
YYYY-MM-DD_guest-name-episode-title.txt
For example: 2024-03-15_varun-krishna-rockets-next-chapter.txt
Configuration
| Environment Variable | Default | Description |
|---|---|---|
MIKENSEY_TRANSCRIPT_DIR |
~/Documents/CodingJourney/Mikensey/context-podcast-transcripts |
Path to folder with .txt transcript files |
Development
npm run dev # Watch mode with tsx
npm run build # Compile TypeScript
npm start # Run compiled server
BYO Transcripts
This server works with any set of transcripts in the expected format. You could adapt it to your own podcast, interview series, or research corpus — just point MIKENSEY_TRANSCRIPT_DIR at your files.
Cost
Zero. Runs entirely locally. No API keys, no external services, no ongoing costs.
License
MIT
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.