How do I use Lenny RAG MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Lenny RAG MCP Server what are the key insights on B2B pricing and growth loops?" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Lenny RAG MCP Server

An MCP server providing hierarchical RAG over 299 Lenny Rachitsky podcast transcripts. Enables product development brainstorming by retrieving relevant insights, real-world examples, and full transcript context.

Quick Start

# Clone the repository (includes pre-built index via Git LFS) git clone git@github.com:mpnikhil/lenny-rag-mcp.git cd lenny-rag-mcp # Create and activate virtual environment python -m venv venv source venv/bin/activate # Install the package pip install -e .

Claude Code

claude mcp add lenny --scope user -- /path/to/lenny-rag-mcp/venv/bin/python -m src.server

Or add to ~/.claude.json:

{ "mcpServers": { "lenny": { "type": "stdio", "command": "/path/to/lenny-rag-mcp/venv/bin/python", "args": ["-m", "src.server"], "cwd": "/path/to/lenny-rag-mcp" } } }

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{ "mcpServers": { "lenny": { "command": "/path/to/lenny-rag-mcp/venv/bin/python", "args": ["-m", "src.server"], "cwd": "/path/to/lenny-rag-mcp" } } }

Cursor

Add to .cursor/mcp.json in your project or ~/.cursor/mcp.json globally:

{ "mcpServers": { "lenny": { "command": "/path/to/lenny-rag-mcp/venv/bin/python", "args": ["-m", "src.server"], "cwd": "/path/to/lenny-rag-mcp" } } }

Replace /path/to/lenny-rag-mcp with your actual clone location in all configs.

MCP Tools

`search_lenny`

Semantic search across the entire corpus. Returns pointers for progressive disclosure.

Parameter	Type	Description
`query`	string	Search query (e.g., "pricing B2B products", "founder mode")
`top_k`	integer	Number of results (default: 5, max: 20)
`type_filter`	string	Filter by type: `insight`, `example`, `topic`, `episode`

Returns: Ranked results with relevance scores, episode references, and topic IDs for drilling down.

`get_chapter`

Load a specific topic with full context. Use after search_lenny to get details.

Parameter	Type	Description
`episode`	string	Episode filename (e.g., "Brian Chesky.txt")
`topic_id`	string	Topic ID (e.g., "topic_3")

Returns: Topic summary, all insights, all examples, and raw transcript segment.

`get_full_transcript`

Load complete episode transcript with metadata.

Parameter	Type	Description
`episode`	string	Episode filename (e.g., "Brian Chesky.txt")

Returns: Full transcript (10-40K tokens), episode metadata, and topic list.

`list_episodes`

Browse available episodes, optionally filtered by expertise.

Parameter	Type	Description
`expertise_filter`	string	Filter by tag (e.g., "growth", "pricing", "AI")

Returns: List of 299 episodes with guest names and expertise tags.

Data Curation Approach

Hierarchical Extraction

Each transcript is processed into a 4-level hierarchy enabling progressive disclosure:

Episode ├── Topics (10-20 per episode) │ ├── Insights (2-4 per topic) │ └── Examples (1-3 per topic)

This allows Claude to start with lightweight search results and drill down only when needed, keeping context windows efficient.

Extraction Schema

{ "episode": { "guest": "Guest Name", "expertise_tags": ["growth", "pricing", "leadership"], "summary": "150-200 word episode summary", "key_frameworks": ["Framework 1", "Framework 2"] }, "topics": [{ "id": "topic_1", "title": "Searchable topic title", "summary": "Topic summary", "line_start": 1, "line_end": 150 }], "insights": [{ "id": "insight_1", "text": "Actionable insight or contrarian take", "context": "Additional context", "topic_id": "topic_1", "line_start": 45, "line_end": 52 }], "examples": [{ "id": "example_1", "explicit_text": "The story as told in the transcript", "inferred_identity": "Airbnb", "confidence": "high", "tags": ["marketplace", "growth", "launch strategy"], "lesson": "Specific lesson from this example", "topic_id": "topic_1", "line_start": 60, "line_end": 85 }] }

Implicit Anchor Detection

Many guests reference companies without naming them ("at my previous company..."). The extraction prompt instructs the model to infer identities based on the guest's background:

Brian Chesky saying "when we started" → Airbnb (high confidence)
A marketplace expert saying "one ride-sharing company" → likely Uber/Lyft (medium confidence)

This surfaces examples that wouldn't be found by keyword search alone.

Quality Thresholds

Each transcript extraction is validated against minimum thresholds:

Element	Minimum	Typical
Topics	10	15-20
Insights	15	25-35
Examples	10	18-25

Extractions below thresholds trigger warnings for manual review.

Models & Tech Stack

Component	Model/Tool	Purpose
Preprocessing	Claude Haiku (via Claude CLI)	Extract structured hierarchy from transcripts
Embeddings	bge-small-en-v1.5	Semantic similarity for search
Vector DB	ChromaDB	Persistent vector storage
MCP Framework	mcp (Python SDK)	Tool interface for Claude

Why Claude Haiku for Preprocessing?

Quality: Haiku follows complex extraction prompts reliably
Cost: ~~$0.02-0.03 per transcript (~~$6-9 total for 299 episodes)
Speed: ~30 seconds per transcript

Why bge-small-en-v1.5 for Embeddings?

Performance: Top-tier retrieval quality for its size
Efficiency: 384 dimensions, fast inference
Local: Runs entirely on CPU, no API calls needed

Corpus Statistics

Metric	Count
Episodes	299
Topics	6,183
Insights	8,840
Examples	6,502
Avg topics/episode	20.7
Avg insights/episode	29.6
Avg examples/episode	21.7

Rebuilding the Index

The repo includes a pre-built ChromaDB index. To rebuild from scratch:

Reprocess Transcripts (requires Claude CLI)

# Process all unprocessed transcripts python scripts/preprocess_haiku.py # Process specific file python scripts/preprocess_haiku.py --file "Brian Chesky.txt" # Parallel processing (4 batches of 50) python scripts/preprocess_haiku.py --limit 50 --offset 0 & python scripts/preprocess_haiku.py --limit 50 --offset 50 & python scripts/preprocess_haiku.py --limit 50 --offset 100 & python scripts/preprocess_haiku.py --limit 50 --offset 150 &

Rebuild Embeddings

# Incremental (only new files) python scripts/embed.py # Full rebuild python scripts/embed.py --rebuild

Project Structure

lenny-rag-mcp/ ├── transcripts/ # 299 raw .txt podcast transcripts ├── preprocessed/ # Extracted JSON hierarchy (one per episode) ├── chroma_db/ # Vector embeddings (Git LFS) ├── prompts/ │ └── extraction.md # Haiku extraction prompt ├── src/ │ ├── server.py # MCP server & tool definitions │ ├── retrieval.py # LennyRetriever class (ChromaDB wrapper) │ └── utils.py # File loading utilities ├── scripts/ │ ├── preprocess_haiku.py # Claude CLI preprocessing │ └── embed.py # ChromaDB embedding pipeline └── pyproject.toml

License

MIT

Lenny RAG MCP Server

Lenny RAG MCP Server

Quick Start

Claude Code

Claude Desktop

Cursor

MCP Tools

`search_lenny`

`get_chapter`

`get_full_transcript`

`list_episodes`

Data Curation Approach

Hierarchical Extraction

Extraction Schema

Implicit Anchor Detection

Quality Thresholds

Models & Tech Stack

Why Claude Haiku for Preprocessing?

Why bge-small-en-v1.5 for Embeddings?

Corpus Statistics

Rebuilding the Index

Reprocess Transcripts (requires Claude CLI)

Rebuild Embeddings

Project Structure

License

Resources

New MCP Servers

Latest Blog Posts

MCP directory API