Indexes and searches plain-text podcast transcripts stored in Dropbox, with automatic syncing of new or changed files.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@dropbox-transcripts-mcp search for 'retention' in transcripts"
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.
An MCP server that indexes plain-text podcast transcripts stored in Dropbox and makes them searchable inside Claude Code. Syncs automatically as new transcripts are added.
Caches all .txt transcripts from a Dropbox folder into a local SQLite database with full-text search (FTS5)
Polls Dropbox every N hours and indexes new or changed files automatically
Exposes four tools to Claude Code: list, retrieve, search, and manual sync
Related MCP server: transcript-search
Python 3.10+
uv (brew install uv on macOS)
A Dropbox account with your transcripts in a folder (default: /Podcasts/Lenny)
Click Create app
Choose Scoped access and Full Dropbox
Name it anything (e.g. transcripts-mcp)
Under the Permissions tab, enable:
files.metadata.read
files.content.read
Copy the App Key and App Secret from the Settings tab
uvx --from git+https://github.com/YOUR_USERNAME/dropbox-transcripts-mcp dropbox-transcripts-setupThis walks you through the OAuth flow and prints your DROPBOX_REFRESH_TOKEN. It will also print the exact MCP config block to paste into your Claude Code settings.
Edit ~/.claude/settings.json and add:
{
"mcpServers": {
"transcripts": {
"command": "uvx",
"args": [
"--from", "git+https://github.com/YOUR_USERNAME/dropbox-transcripts-mcp",
"dropbox-transcripts-mcp"
],
"env": {
"DROPBOX_APP_KEY": "your_app_key",
"DROPBOX_APP_SECRET": "your_app_secret",
"DROPBOX_REFRESH_TOKEN": "your_refresh_token",
"DROPBOX_FOLDER_PATH": "/Podcasts/Lenny"
}
}
}
}The first time Claude Code starts the server it will sync all transcripts from Dropbox. Subsequent syncs happen automatically in the background every 6 hours (configurable).
All configuration is via environment variables:
Variable | Required | Default | Description |
| yes | Dropbox app key | |
| yes | Dropbox app secret | |
| yes | OAuth refresh token | |
| no |
| Path to transcripts folder in Dropbox |
| no |
| How often to poll Dropbox for changes |
| no |
| Local SQLite database path |
Transcript files should be named Guest Name.txt. The filename (without .txt) becomes the episode identifier used by get_episode and shown in list_episodes.
Examples: Adam Fishman.txt, Elena Verna 2.0.txt
Tool | Description |
| List all indexed episodes with last sync time |
| Get a full transcript by guest name (supports partial match) |
| Full-text search with highlighted snippets. Supports quoted phrases, AND/OR, prefix wildcards ( |
| Manually trigger a Dropbox sync |
MIT
Get the full transcript for an episode by guest name. Supports partial matches: 'Fishman' will find 'Adam Fishman'.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Discloses partial match behavior but lacks details on auth, rate limits, or side effects. Adequate but not comprehensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no filler. Purpose and key behavior stated efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Tool is simple (1 param, output schema exists). Description covers how to use and partial match behavior; output schema handles return format details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage; description compensates by specifying 'guest name' and giving a concrete example of partial matching, adding essential meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clear verb ('Get'), resource ('full transcript'), and method ('by guest name'). Distinguishes from siblings like list_episodes_tool, search_transcripts_tool, sync_tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implicitly states when to use: when you have a guest name and need a transcript. Lacks explicit exclusions or alternatives, but context from sibling names makes it clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all available podcast transcript episodes.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It only states 'list all available' without disclosing behavioral traits (e.g., pagination, rate limits, whether it's a snapshot). Minimal transparency beyond the tool name.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short sentence that is front-loaded with the essential purpose. Every word earns its place with no verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (no parameters, output schema exists), the description is mostly adequate. It could hint at the return structure (list of episodes), but the output schema covers that. Missing details like order or limits are minor.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, and schema coverage is 100%. Baseline for zero parameters is 4. The description adds context about the resource type but does not need to elaborate further.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'List' and the resource 'all available podcast transcript episodes'. It distinguishes from siblings like get_episode_tool (single episode) and search_transcripts_tool (search).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving all episodes but offers no explicit guidance on when to use this tool versus alternatives like get_episode_tool or search_transcripts_tool. No when-not or prerequisite information is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Full-text search across all transcript content. Returns matching episodes with highlighted snippets. Supports quoted phrases, AND/OR operators, and prefix wildcards (e.g. 'retain*').
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| limit | No |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses query syntax features (quoted phrases, operators, wildcards), which is helpful. However, it omits other behavioral aspects like whether it is read-only, pagination behavior, or performance implications.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three concise sentences: purpose, output, and supported features. No superfluous information, well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema (not shown) and simple parameters, the description covers purpose, output type, and query syntax. It lacks details on result ordering or pagination, but overall sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, so the description must compensate. It explains query capabilities but does not describe the limit parameter beyond its default. Partial compensation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it performs full-text search across all transcript content and returns matching episodes with highlighted snippets. It distinguishes itself from sibling tools like get_episode, list_episodes, and sync by specifying search capabilities.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description does not mention when not to use it or refer to sibling tools for specific use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Manually trigger a sync from Dropbox to update the local transcript index.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses the operation (sync, update index) but does not mention potential side effects, auth requirements, idempotency, or if it's a long-running operation. Moderate transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
One sentence that is front-loaded with the action and completely captures the tool's purpose without any fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Simple tool with no parameters and an output schema (though not detailed). Description is complete enough for a trigger tool; it could note if async or idempotent, but not necessary for basic use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters in schema, and schema coverage is 100%. Baseline for 0 parameters is 4. Description adds no parameter info because none exist, but that is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the action (trigger sync), source (Dropbox), and effect (update local index). Distinguishes from siblings which deal with episodes and transcripts, not sync operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Manually trigger a sync', implying when to use: when the local index needs updating from Dropbox. Does not mention when not to use or provide alternatives, but context from sibling tools helps.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each tool has a clearly distinct purpose: listing episodes, retrieving a specific transcript, full-text search, and triggering a sync. No overlap or ambiguity.
Most tools follow a verb_noun_tool pattern (get_episode_tool, list_episodes_tool, search_transcripts_tool, sync_tool). However, there is inconsistency in pluralization: 'get_episode' (singular) vs. 'list_episodes' and 'search_transcripts' (plural).
Four tools is well-scoped for a server focused on podcast transcripts. Each tool serves a necessary function without redundancy or deficiency.
The tool set covers all core operations for a transcript reader: listing available episodes, retrieving a specific transcript, searching across content, and syncing the index. No obvious gaps for the intended use case.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
An MCP server that provides tools to discover and retrieve podcast episodes transcripts.
MCP server for structured access to Lenny Rachitsky podcast transcripts. For content creators.
MCP server for RiverScript, an AI transcription platform - fetches transcripts shared via a link.
Hosted MCP server connecting claude.ai, ChatGPT and other AI apps to your own computer
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/pedraum/dropbox-transcripts-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server
