meetings-mcp

An MCP server over the output of my meeting-transcription-pipeline. It takes the finished transcripts (Whisper text, speaker labeled with pyannote) and exposes them as tools any MCP-capable AI client can call.

The pipeline turns a recording into a long text file. Thats useful, but nobody rereads two hours of meeting. With this server a model can list the meetings, page through one, or search all of them for a topic and come back with the exact timestamp.

Tools

Setup

pipx install meetings-mcp

uvx meetings-mcp works too if you prefer no install. Hacking on a clone instead:

python3 -m venv .venv
.venv/bin/pip install mcp

Wire it into Claude Code:

claude mcp add meetings -- meetings-mcp

Running from a clone, point it at the venv instead:

claude mcp add meetings -- /path/to/meetings-mcp/.venv/bin/python /path/to/meetings-mcp/meetings_mcp.py

Demo

What it looks like from Claude Code:

> did we talk about the renewal in any recent meeting?

⏺ meetings · list_meetings()

  2 meeting(s):
  - 2026-09-21-vendor-review: 00:41:09, 4 turns, 3 speaker(s)
  - 2026-09-24-weekly-sync: 00:18:47, 3 turns, 2 speaker(s)

⏺ meetings · search_transcripts("renewal")

  3 match(es) for 'renewal':

  [2026-09-21-vendor-review @ 00:03:12, turn 1] Speaker 2: The renewal quote came in higher, about fifteen percent.
  [2026-09-21-vendor-review @ 00:07:40, turn 2] Speaker 1: Then we push back on the renewal before Friday.
  [2026-09-24-weekly-sync @ 00:12:30, turn 1] Speaker 2: The renewal is signed, same price as last year.

⏺ Yes, twice. In the vendor review the quote came in about 15% higher
  and the plan was to push back. By the weekly sync it was signed at
  last year's price.

The meetings above are demo data. The format is exactly what the server returns.

Design notes

• Read-only by construction. Every tool reads text files, nothing writes. The transcripts stay on my machine and the model only sees what a tool returns.
• Pseudonyms at the boundary. Speaker labels become Speaker 1, Speaker 2 and so on by first appearance, even when the source file has real names. Emails, phone numbers, home paths and long tokens get replaced before anything crosses the protocol.
• Paged, not dumped. A two hour meeting is hundreds of turns. The server hands it over 25 turns at a time so a model reads what it needs instead of flooding its context.

Honest notes

• It reads the pipeline's transcripts/ folder, one .speakers.txt per meeting, falling back to the raw Whisper .txt when diarization hasnt run. You need to run the pipeline first. The default path is my local copy. Point MEETINGS_DATA_DIR at your own data folder.
• The redaction is pattern based. It catches speaker labels, emails, phone numbers and paths, but a name spoken inside a sentence still comes through as said.
• Search is a plain substring match, so an Arabic query has to match the spelling Whisper produced.
• The logic is tested against synthetic transcripts in test_server.py. Run python test_server.py.

About

Al Amin Bashir Afara, Dubai · github.com/aminafara123 · linkedin.com/in/aminafara