Lenny's Podcast MCP

search_transcripts

Search podcast transcripts to find expert insights on product management, growth, pricing, and hiring topics from industry leaders.

Instructions

Search across all of Lenny's Podcast transcripts for insights on a topic. Returns relevant excerpts from episodes with guest names. Use this to find what product leaders and experts have said about specific topics like pricing, growth, product management, hiring, etc.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "search_transcripts"
  }
}

Input Schema

Name	Required	Description	Default
`query`	Yes	The search query - use keywords related to the topic you want insights on
`limit`	No	Maximum number of results to return (default: 10)

Schema Changelog

Changes observed during successful MCP inspections.

First observedJan 27, 2026

TDQS

A4/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the search scope ('across all transcripts') and return format ('relevant excerpts from episodes with guest names'), but lacks details on permissions, rate limits, error handling, or pagination behavior. The description doesn't contradict any annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured in two sentences with zero waste. The first sentence states the purpose and return format, while the second provides usage context and examples. Every sentence earns its place by adding value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (search functionality with 2 parameters), no annotations, and no output schema, the description does a good job covering purpose, usage, and return format. However, it could be more complete by addressing behavioral aspects like search algorithm characteristics or result ordering.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds marginal value by implying the query should be topic-based ('insights on a topic') and mentioning example topics, but doesn't provide additional syntax or format details beyond what the schema provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('search across transcripts') and resources ('Lenny's Podcast transcripts'), and distinguishes it from sibling tools by focusing on search functionality rather than retrieving specific episodes or listing them. It explicitly mentions returning 'relevant excerpts from episodes with guest names'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool ('to find what product leaders and experts have said about specific topics'), including example topics. However, it does not explicitly state when NOT to use it or mention alternatives like sibling tools (get_episode, list_episodes) for different use cases.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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