Do I need an API key?

No authentication details are specified; access is via the provided streamable-http transport.

Can I search across all episodes?

Yes, the server supports structured queries over the full transcript collection.

Lenny Rachitsky Podcast Transcripts MCP Server

Verified

Structured access to Lenny Rachitsky podcast transcripts for content creators.

MCP ServerOtherRemote (streamable-http) 8

View on GitHub

Updated 2026-06-16

What is the Lenny Rachitsky Podcast Transcripts MCP Server MCP server?

The server enables querying and retrieving podcast transcripts in a structured format over streamable-http transport. It targets users who need reliable access to spoken content for research, repurposing, or analysis.

Built for integration with AI clients, it supports content workflows without requiring manual transcript handling or external parsing tools.

Install & connect

Add this to your MCP client config. Pick your client below and copy.

{
  "mcpServers": {
    "lenny-rachitsky-podcast": {
      "url": "https://lenny-rachitsky.run.mcp.com.ai/mcp"
    }
  }
}

Example prompts

Once connected, try asking your AI client:

“Find key insights from the episode with Paul Graham

“Extract quotes about product-led growth from recent transcripts

“Summarize discussions on startup hiring across 2023 episodes

“List all mentions of pricing strategies in the Lenny's Newsletter podcast

Security & permissions

Remote streamable-http server; requires no local installation or API keys but transmits transcript queries to the hosted endpoint.

What you can do with Lenny Rachitsky Podcast Transcripts MCP Server

Episode research

Quickly locate specific discussions or quotes across multiple episodes.

Content repurposing

Extract segments for newsletters, social posts, or summaries.

Guest analysis

Review recurring themes from interviews with founders and operators.

How to use Lenny Rachitsky Podcast Transcripts MCP Server

1Connect your MCP client to the streamable-http endpoint.
2Browse available transcript resources in the client interface.
3Issue natural language queries for specific episodes or topics.
4Review returned structured transcript segments in your workflow.
5Iterate with follow-up prompts for deeper extraction.