Zoom Transcript

About this Protocol

The Zoom Transcript MCP server acts as a smart bridge between Zoom meeting recordings and AI assistants. It allows users to bring their meeting conversations directly into their AI chat environment, making it possible to ask questions about past discussions, summarize key decisions, or find specific details from a video call without having to manually watch recordings or copy-paste text. By automating the retrieval of meeting data, it turns a library of Zoom recordings into a searchable, interactive knowledge base for any compatible LLM. Beyond simple fetching, this tool offers a robust set of features for managing transcript data. It can list available recordings within specific date ranges, identify participants, and automatically download transcripts from the most recent meetings. The server handles the heavy lifting of organizing these files into a structured system, categorizing them by month and including detailed metadata like meeting IDs, topics, and durations. This organization ensures that transcripts are not just downloaded, but are categorized in a way that remains easy for both humans and machines to navigate. For developers and technical users, the server provides four primary tools—`list_meetings`, `download_transcript`, `get_recent_transcripts`, and `search_transcripts`—which an AI model can invoke autonomously to find information. It utilizes Zoom’s Server-to-Server OAuth for secure authentication and stores data in a clean directory structure with both VTT transcript files and JSON metadata. This structured approach is particularly valuable for building RAG (Retrieval-Augmented Generation) workflows, as it allows an AI to perform targeted searches across large volumes of meeting history to provide contextually accurate answers based on real-world conversations.

How to Use

1. Installation

Prerequisites:
* Node.js (v16 or higher)
* Zoom Account with Cloud Recording enabled
* Zoom OAuth App credentials

Steps:
1. Clone the repository:
bash git clone https://github.com/yourusername/zoom_transcript_mcp.git cd zoom_transcript_mcp
2. Install dependencies:
bash npm install
3. Build the project:
bash npm run build

2. Configuration

To configure the server for MCP clients (like Claude Desktop or Cursor), add the following to your mcpServers configuration file:

{
  "mcpServers": {
    "zoom-transcripts": {
      "command": "node",
      "args": ["/path/to/zoom-transcripts-server/build/index.js"],
      "env": {
        "ZOOM_ACCOUNT_ID": "your_zoom_account_id",
        "ZOOM_CLIENT_ID": "your_zoom_client_id",
        "ZOOM_CLIENT_SECRET": "your_zoom_client_secret",
        "TRANSCRIPTS_DIR": "/path/to/transcripts/directory"
      }
    }
  }
}

Obtaining Zoom Credentials:
1. Sign in to the Zoom App Marketplace.
2. Click "Develop" > "Build App" and choose Server-to-Server OAuth.
3. Under Scopes, add:
* cloud_recording:read:list_account_recordings:admin
* cloud_recording:read:recording:admin
* cloud_recording:read:list_user_recordings:admin
4. Activate the app and collect your Account ID, Client ID, and Client Secret.

3. Available Tools

list_meetings: Lists available Zoom meetings with recordings.
- Parameters: dateRange (object with from and to), participant (optional string).
download_transcript: Downloads a transcript for a specific meeting.
- Parameters: meetingId (string: Meeting ID or UUID).
get_recent_transcripts: Downloads transcripts from recent meetings.
- Parameters: count (number: default 5).
search_transcripts: Searches across all downloaded transcripts for specific content.
- Parameters: query (string), dateRange (optional object with from and to).

4. Example Prompts

Searching for specific content in transcripts:

"Search my Zoom transcripts for any mention of 'project timeline'."

Manual tool call example for Claude:

<use_mcp_tool>
<server_name>zoom-transcripts</server_name>
<tool_name>search_transcripts</tool_name>
<arguments>
{
  "query": "project timeline"
}
</arguments>
</use_mcp_tool>

Listing meetings within a date range:

"List all my Zoom meetings with recordings from January 1st, 2025 to March 31st, 2025."

Use Cases

Use Case 1: Automated Meeting Summarization and Action Item Extraction

Problem: Professionals often spend hours in back-to-back Zoom meetings and struggle to find the time to manually document key decisions, specific action items, and the owners of those tasks.
Solution: This MCP allows an AI assistant to directly fetch the transcript of a meeting immediately after it ends. The AI can then process the text to generate a concise summary and a structured list of follow-up tasks without the user having to download and upload files manually.
Example: "Claude, download the transcript for today's 'Sprint Planning' meeting and create a table of all action items, who they were assigned to, and any mentioned deadlines."

Use Case 2: Cross-Meeting Knowledge Retrieval

Problem: Important decisions or project requirements are often discussed in meetings but forgotten weeks later. Finding the specific recording and scrubbing through video to find a 30-second explanation is incredibly time-consuming.
Solution: By using the search_transcripts tool, users can query their entire library of past Zoom discussions for specific keywords, technical terms, or project names.
Example: "Search through all my meetings from the last three months for any mention of 'database migration' and tell me what the final decision was regarding the cut-over date."

Use Case 3: Rapid Onboarding and Catch-up

Problem: When a team member returns from a long vacation or a new hire joins a project, they need to quickly understand the context of what has been discussed in their absence across multiple sessions.
Solution: The get_recent_transcripts tool can fetch the last several transcripts in one go. An AI can then synthesize these into a "Project Status Briefing," highlighting the evolution of a project's goals and current roadblocks.
Example: "Download the transcripts for the last five 'Marketing Strategy' meetings and give me a high-level overview of how our social media strategy has changed over the past month."

Use Case 4: Qualitative Analysis for Sales and Support

Problem: Sales managers and product owners need to understand customer pain points mentioned during calls, but they cannot listen to every single customer interaction.
Solution: The MCP enables an AI to act as a research assistant that audits transcripts for specific sentiment, recurring objections, or feature requests mentioned by participants.
Example: "List the meetings from last week where 'competitor pricing' was mentioned. For each, give me a quote from the participant explaining their concern and summarize our team's response."

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Added Dec 27, 2025