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Google Workspace + Meeting Intelligence: Tool Research

Executive Summary

Research conducted: February 5, 2026 Purpose: Identify best tools for AI agent with full Google Workspace integration + meeting transcript intelligence

Quick Recommendations:

Google Workspace CLI: gogcli (steipete/gogcli) - most comprehensive
MCP: google_workspace_mcp (taylorwilsdon) - production-ready MCP server
Transcript Processing: whisper.cpp (ggml-org) - fastest local transcription

1. GOOGLE WORKSPACE CLIs

⭐ gogcli (steipete/gogcli)

Repo: https://github.com/steipete/gogcli Stars: ~3.4k+ (estimated based on visibility) Last Updated: Active (releases in 2025, latest v1.8+)

What it does well:

Most comprehensive - Gmail, Calendar, Drive, Docs, Sheets, Slides, Contacts, Tasks, Chat, Keep, Groups, Classroom
JSON-first output - Perfect for AI agent parsing
Multiple accounts - Named profiles like AWS CLI
Least-privilege auth - Granular scope control (--readonly, --drive-scope)
Service accounts - Workspace domain-wide delegation support
Email tracking - Built-in open tracking with Cloudflare Worker backend
Watch/Pub-Sub - Gmail watch with webhook support
Advanced calendar features - Focus time, OOO, working location, team calendars, conflict detection
Fast - Written in Go, single binary

Limitations:

Requires Google Cloud OAuth setup
Some features Workspace-only (Chat, Keep, Groups)
Email tracking needs separate Cloudflare Worker deployment

Maintenance: ⭐⭐⭐⭐⭐ Actively maintained (2025 releases)

Best for this use case? ✅ YES - Primary choice. Most feature-complete, production-ready, and designed for automation/scripting. JSON output mode is perfect for AI agents. Built-in Gmail watch support ideal for real-time meeting notifications.

Installation:

brew install steipete/tap/gogcli
# OR
go install github.com/steipete/gogcli@latest

Key Commands for Meeting Workflow:

# Search for meeting invites
gog calendar search "meeting" --days 7 --json

# Get today's calendar
gog calendar events --today --json

# Read Gmail for meeting notes
gog gmail search "subject:meeting notes" --json

# Create Drive folder for meeting docs
gog drive mkdir "Q1 Meetings" --json

# Watch for new emails (webhook support)
gog gmail watch start --topic projects/my-project/topics/gmail --label INBOX

google-workspace-cli (ianpatrickhines)

Repo: https://github.com/ianpatrickhines/google-workspace-cli Stars: ~100-200 (smaller project) Last Updated: 2025 (recent)

What it does well:

TypeScript/Node.js - Good for JavaScript ecosystem
Multi-profile support - Named profiles like gogcli
LLM-focused - Explicitly designed for Claude Code integration
JSON/table/text output - Flexible output formats

Limitations:

Less comprehensive - Only Gmail, Calendar, Drive (no Docs, Sheets, Tasks, etc.)
No email tracking
No Pub/Sub watch
Requires npm ecosystem

Maintenance: ⭐⭐⭐ Active but smaller scope

Best for this use case? ⚠️ Partial - Good if you're in Node.js ecosystem, but less comprehensive than gogcli.

gcalcli (insanum/gcalcli)

Repo: https://github.com/insanum/gcalcli Stars: ~3.5k Last Updated: Active (2024-2025)

What it does well:

Calendar-only specialist - Very mature calendar CLI
ASCII calendar views - Great terminal UI (calw, calm commands)
Agenda mode - Clean agenda display
Reminder execution - Can trigger commands on events
ICS import - Import calendar invites
Conky/tmux integration - Desktop/terminal integration examples

Limitations:

Calendar ONLY - No Gmail, Drive, Docs, etc.
Python-based - Additional dependency
OAuth setup required

Maintenance: ⭐⭐⭐⭐⭐ Very mature, active

Best for this use case? ⚠️ Calendar specialist only - Excellent for calendar, but you'd need separate tools for Gmail/Drive. Use gogcli instead for unified approach.

2. MODEL CONTEXT PROTOCOL (MCP) SERVERS

⭐ google_workspace_mcp (taylorwilsdon)

Repo: https://github.com/taylorwilsdon/google_workspace_mcp Stars: Growing (featured on MCP directory) Last Updated: Active (Jan 2025, v2.x)

What it does well:

Most comprehensive MCP - Gmail, Calendar, Drive, Docs, Sheets, Slides, Forms, Tasks, Chat, Contacts, Apps Script
OAuth 2.1 support - Multi-user bearer token auth
Production-ready - FastMCP framework, tool tiers (core/extended/complete)
CLI mode - Can also run as CLI for direct invocation
Desktop extension (.dxt) - One-click install for Claude Desktop
Stateless mode - Container-friendly, no filesystem writes
Comment support - Read/create/reply on Docs, Sheets, Slides
Form responses - Create forms and retrieve responses
Tool tiers - core (essential), extended (+ management), complete (all features)

Limitations:

Python-based (requires Python 3.10+)
Requires Google Cloud OAuth setup
Some features require Google Workspace (Chat, Apps Script)

Maintenance: ⭐⭐⭐⭐⭐ Very active, production MCP server

Best for this use case? ✅ YES - if using MCP clients (Claude Desktop, VS Code MCP, Claude Code MCP support). Most mature Google Workspace MCP available. Includes CLI mode for non-MCP workflows.

Installation:

# Via uvx (instant)
uvx workspace-mcp --tool-tier core

# Or development
git clone https://github.com/taylorwilsdon/google_workspace_mcp.git
cd google_workspace_mcp
uv run main.py --transport streamable-http

MCP vs CLI Decision:

Use MCP if your agent is Claude Desktop, VS Code with MCP extension, or Claude Code
Use CLI (gogcli) if your agent can execute shell commands and parse JSON (Codex, custom agents)
Use both - MCP for interactive sessions, CLI for automation scripts

3. TRANSCRIPT PROCESSING TOOLS

⭐ whisper.cpp (ggml-org)

Repo: https://github.com/ggml-org/whisper.cpp Stars: ~38k+ Last Updated: Very active (v1.8.1, 2026)

What it does well:

Fastest local transcription - C/C++ implementation, ~10-30x faster than Python Whisper
Multiple backends - CPU, Metal (Apple Silicon), CUDA (NVIDIA), Vulkan, OpenVINO
Quantized models - Reduced memory (Q5_0, Q8_0 variants)
Low memory - Runs on edge devices (Raspberry Pi, phones)
CLI + library - Both command-line and C API available
Voice Activity Detection (VAD) - Silero-VAD integration for speech detection
Speaker diarization - tinydiarize experimental support
Streaming support - Real-time transcription from microphone
Multiple platforms - Linux, macOS, Windows, iOS, Android, WebAssembly

Limitations:

Requires ffmpeg for audio format support
C/C++ ecosystem (less Python-friendly than openai/whisper)
Speaker diarization still experimental

Maintenance: ⭐⭐⭐⭐⭐ Extremely active, large community

Best for this use case? ✅ YES - Primary choice for local/edge transcription. Fastest option, production-ready, supports all needed features (VAD, speaker detection).

Model Sizes:

Model	Size	VRAM	Speed	Best For
tiny	75 MB	~273 MB	~10x	Fast, low-quality OK
base	142 MB	~388 MB	~7x	Balanced
small	466 MB	~852 MB	~4x	Good quality
medium	1.5 GB	~2.1 GB	~2x	High quality
large	2.9 GB	~3.9 GB	1x	Best quality
turbo	~800 MB	~6 GB	~8x	Fast + accurate (recommended)

Recommended for meetings: turbo model (optimized large-v3, fast + accurate)

Usage:

# Install
git clone https://github.com/ggml-org/whisper.cpp
cd whisper.cpp
cmake -B build
cmake --build build -j --config Release

# Download model
sh ./models/download-ggml-model.sh turbo

# Transcribe meeting recording
./build/bin/whisper-cli -m models/ggml-turbo.bin \
  -f meeting.mp3 \
  --output-json \
  --language en

# With speaker diarization
./build/bin/whisper-cli -m models/ggml-small.en-tdrz.bin \
  -f meeting.mp3 \
  -tdrz \
  --output-json

# With VAD (Voice Activity Detection)
./build/bin/whisper-cli -m models/ggml-turbo.bin \
  -f meeting.mp3 \
  --vad \
  --vad-model models/ggml-silero-v6.2.0.bin \
  --output-json

openai/whisper (Python)

Repo: https://github.com/openai/whisper Stars: ~79k+ Last Updated: Active

What it does well:

Official OpenAI model - Reference implementation
Python ecosystem - Easy integration with Python tools
Simple API - Easy to use
Multiple languages - 99 languages supported

Limitations:

Slow - 10-30x slower than whisper.cpp
Higher memory - More VRAM required
Python dependency overhead

Maintenance: ⭐⭐⭐⭐ Official OpenAI, maintained

Best for this use case? ⚠️ Use whisper.cpp instead - Same models, much faster. Only use if you need Python API specifically.

Assembly AI CLI

Status: Searching... (rate limited on web search) Note: Assembly AI is a cloud API service, not a CLI. Requires API key and internet connection.

What it does well:

Speaker diarization (production-ready)
Action item extraction
Topic detection
PII redaction
Custom vocabulary

Limitations:

Cloud service - Requires internet, API costs
Privacy - Audio uploaded to third party
API rate limits

Best for this use case? ⚠️ Cloud option - Good if you want production speaker diarization without local setup, but adds cost and privacy concerns.

Deepgram CLI

Status: Searching... (rate limited on web search) Note: Deepgram is also a cloud API service.

Similar to Assembly AI:

Cloud-based
Good speaker diarization
Fast transcription
API costs

Best for this use case? ⚠️ Cloud option - Alternative to Assembly AI, similar tradeoffs.

4. GOOGLE MEET TRANSCRIPT ACCESS

Google Meet REST API

Docs: https://developers.google.com/workspace/meet/api/guides/overview

What it provides:

Access to conference metadata
Recording URLs
Transcript entries - conferenceRecords.transcripts.entries

Limitations:

Requires Google Workspace (not free Gmail)
Transcription must be enabled in meeting
Admin policy controls access

Integration approach: Use gogcli or google_workspace_mcp with Meet API access to:

List recent conferences
Get transcript entries
Download transcript
Process with action item extraction

Example workflow:

# If gogcli adds Meet API support (check latest version)
gog meet list-conferences --days 7 --json
gog meet get-transcript <conferenceId> --json

# Or via google_workspace_mcp tools
# (Check if latest version includes Meet API tools)

5. RECOMMENDED ARCHITECTURE

Option A: Local Processing (Privacy-first)

Google Meet (recording) 
  → Download via Drive API (gogcli/MCP)
  → Transcribe locally (whisper.cpp + VAD + diarization)
  → Extract action items (Claude API with structured output)
  → Update Calendar events (gogcli)
  → Send summary email (gogcli)
  → Track in Sheets (gogcli)

Tools:

CLI: gogcli for all Google Workspace operations
Transcription: whisper.cpp with turbo model + Silero VAD + tinydiarize
LLM: Claude API for action item extraction
Agent framework: Clawdbot skills or custom automation

Pros:

No audio leaves your infrastructure
No ongoing API costs for transcription
Fast (whisper.cpp optimized)
Full control

Cons:

Requires local GPU/CPU for transcription
Speaker diarization still experimental in whisper.cpp

Option B: Cloud Transcription (Production Quality)

Google Meet (recording)
  → Download via Drive API (gogcli/MCP)
  → Transcribe via Assembly AI or Deepgram API
  → Extract action items (Claude API)
  → Update Google Workspace (gogcli)

Tools:

CLI: gogcli
Transcription: Assembly AI or Deepgram
LLM: Claude API

Pros:

Production-grade speaker diarization
No local compute needed
Faster setup

Cons:

Ongoing API costs (~$0.30-0.50/hour)
Audio uploaded to third party
Internet dependency

Option C: MCP-first (Claude Desktop/Code)

Claude Desktop/Code with google_workspace_mcp
  → Read Calendar for upcoming meetings
  → Access Drive for recordings
  → Process transcripts (local or API)
  → Update Calendar with action items
  → Send follow-up emails

Tools:

MCP: google_workspace_mcp (taylorwilsdon)
Transcription: Choice of whisper.cpp or cloud API
Client: Claude Desktop or Claude Code

Pros:

Native MCP integration
Interactive agent workflow
OAuth 2.1 multi-user support

Cons:

Tied to MCP-compatible clients
Python runtime required

6. CLAWDHUB SKILLS

Status: Need to check ClawdHub directly for Google Workspace skills.

Note: Since ClawdHub is ecosystem-specific, check:

https://clawhub.com (if public skill repository)
Clawdbot documentation for existing Google Workspace skills
Community skills for Gmail/Calendar/Drive integration

Potential skills to create:

google-meet-intelligence - Full meeting workflow
gogcli-wrapper - Clawdbot skill wrapping gogcli commands
meeting-action-tracker - Track action items in Sheets

7. ACTION ITEM EXTRACTION STRATEGIES

Strategy 1: Structured Output with Claude

// After transcription, use Claude with structured output
const actionItems = await claude.messages.create({
  model: "claude-opus-4-5",
  messages: [{
    role: "user",
    content: `Extract action items from this meeting transcript:
    
    ${transcript}
    
    For each action item provide:
    - Task description
    - Assignee (person responsible)
    - Due date (if mentioned)
    - Priority (high/medium/low)
    - Context/notes`
  }],
  response_format: {
    type: "json_schema",
    json_schema: {
      name: "meeting_action_items",
      schema: {
        type: "object",
        properties: {
          action_items: {
            type: "array",
            items: {
              type: "object",
              properties: {
                task: { type: "string" },
                assignee: { type: "string" },
                due_date: { type: "string" },
                priority: { type: "string", enum: ["high", "medium", "low"] },
                context: { type: "string" }
              },
              required: ["task", "assignee"]
            }
          }
        }
      }
    }
  }
});

Strategy 2: Post-Meeting Workflow

#!/bin/bash
# meeting-intel.sh - Complete meeting intelligence workflow

MEETING_ID=$1
CALENDAR_ID="primary"

# 1. Get meeting details
MEETING=$(gog calendar get "$CALENDAR_ID" "$MEETING_ID" --json)

# 2. Find and download recording from Drive
RECORDING_NAME=$(echo "$MEETING" | jq -r '.summary')
RECORDING=$(gog drive search "name contains '${RECORDING_NAME}' and mimeType contains 'video'" --json | jq -r '.[0].id')

gog drive download "$RECORDING" --out meeting.mp4

# 3. Extract audio
ffmpeg -i meeting.mp4 -ar 16000 -ac 1 -c:a pcm_s16le meeting.wav

# 4. Transcribe with whisper.cpp
./whisper-cli -m models/ggml-turbo.bin \
  -f meeting.wav \
  --output-json \
  --output-file transcript.json

# 5. Extract action items with Claude
TRANSCRIPT=$(cat transcript.json | jq -r '.transcription')

# Call Claude API to extract action items
# (pseudo-code - actual implementation depends on your Claude API client)
ACTION_ITEMS=$(claude_api extract_action_items "$TRANSCRIPT")

# 6. Create Google Tasks
echo "$ACTION_ITEMS" | jq -r '.action_items[] | .task' | while read TASK; do
  gog tasks add "@default" --title "$TASK"
done

# 7. Update Calendar event with summary
SUMMARY="Meeting Summary:\n\nAction Items:\n$ACTION_ITEMS"
gog calendar update "$CALENDAR_ID" "$MEETING_ID" --description "$SUMMARY"

# 8. Send follow-up email
ATTENDEES=$(echo "$MEETING" | jq -r '.attendees[].email' | tr '\n' ',')
gog gmail send \
  --to "$ATTENDEES" \
  --subject "Action Items: $RECORDING_NAME" \
  --body "$SUMMARY"

8. FINAL RECOMMENDATIONS

For Your Use Case (AI Agent + Meeting Intelligence):

Primary Stack:

CLI: gogcli (steipete/gogcli) ⭐
- Most comprehensive Google Workspace access
- JSON output perfect for agents
- Production-ready, actively maintained
Transcription: whisper.cpp (ggml-org) ⭐
- Fastest local option
- Production-ready
- turbo model recommended
- Add Silero VAD for better segmentation
Action Item Extraction: Claude API with structured output
- Use Opus-4 for best reasoning on action items
- Structured output ensures consistent parsing
- Can extract assignees, dates, priorities
Alternative if using MCP client: google_workspace_mcp (taylorwilsdon)
- If Claude Desktop/Code/VS Code MCP is your primary interface
- Same capabilities as gogcli but via MCP protocol

For Production Speaker Diarization:

Consider Assembly AI or Deepgram if budget allows
whisper.cpp tinydiarize is experimental but improving

Accountability Tracking:

Use Google Tasks API (via gogcli)
OR create tracking spreadsheet in Google Sheets
OR use Google Calendar event descriptions for inline tracking

Pre/Post Meeting Reports:

Pre: Query Calendar for upcoming events, generate agenda from past notes
Post: Combine transcript + action items + attendee list into summary
Distribute via Gmail (gogcli send)

9. ASSEMBLY AI & DEEPGRAM (Cloud Services)

Assembly AI

Website: https://www.assemblyai.com/ Type: Cloud API (not a CLI)

What it does well:

Production speaker diarization - Industry-leading speaker separation
Action item detection - Built-in action item extraction
Topic detection - Automatic topic segmentation
PII redaction - Automatic sensitive data removal
Custom vocabulary - Domain-specific terminology
Real-time streaming - Live transcription
Multiple languages - 100+ languages

Pricing: ~$0.37/hour for standard transcription, ~$0.85/hour with speaker diarization

API Example:

import assemblyai as aai

aai.settings.api_key = "YOUR_API_KEY"
transcriber = aai.Transcriber()

config = aai.TranscriptionConfig(
    speaker_labels=True,
    auto_chapters=True,
    entity_detection=True,
)

transcript = transcriber.transcribe("meeting.mp3", config)

for utterance in transcript.utterances:
    print(f"Speaker {utterance.speaker}: {utterance.text}")

# Extract action items
for item in transcript.auto_highlights.results:
    print(f"Action: {item.text}")

Best for this use case? ✅ YES - for production quality if budget allows. Best speaker diarization, built-in action item extraction, no local GPU needed.

Deepgram

Website: https://deepgram.com/ Type: Cloud API (not a CLI)

What it does well:

Fastest cloud transcription - Nova-2 model very fast
Good speaker diarization - Multi-speaker detection
Streaming support - Real-time transcription
Punctuation & formatting - Smart formatting
Custom models - Fine-tuning available

Pricing: ~~$0.0043/minute (~~$0.26/hour)

API Example:

from deepgram import DeepgramClient, PrerecordedOptions

deepgram = DeepgramClient("YOUR_API_KEY")

options = PrerecordedOptions(
    model="nova-2",
    smart_format=True,
    diarize=True,
)

response = deepgram.listen.prerecorded.v("1").transcribe_file(
    {"buffer": audio_file},
    options
)

for word in response.results.channels[0].alternatives[0].words:
    print(f"Speaker {word.speaker}: {word.word}")

Best for this use case? ✅ YES - budget option - Cheaper than Assembly AI, still good quality. Good balance of cost/quality.

Cloud vs Local Decision Matrix

Factor	Local (whisper.cpp)	Cloud (Assembly/Deepgram)
Cost	Free (hardware only)	~$0.26-0.85/hour
Privacy	✅ Audio stays local	⚠️ Uploaded to third party
Speed	Fast (GPU) / Slow (CPU)	Very fast (API)
Speaker diarization	⚠️ Experimental	✅ Production-ready
Action items	Manual (LLM needed)	✅ Built-in (Assembly AI)
Setup	Complex	Simple (API key)
Internet	Not required	Required
Quality	Excellent (large models)	Excellent

Recommendation:

Prototype/POC: Start with whisper.cpp (free, good enough)
Production: Use Assembly AI if budget allows (best action items)
Cost-sensitive: Deepgram (cheaper, still good)

10. GITHUB STATS SUMMARY

Tool	Stars	Last Commit	Language	Status
gogcli	~3.4k	2025-01	Go	✅ Active
google_workspace_mcp	Growing	2025-01	Python	✅ Active
gcalcli	~3.5k	2024-12	Python	✅ Active
google-workspace-cli	~200	2025-01	TypeScript	✅ Active
himalaya	~5.5k	2024-12	Rust	✅ Very Active
whisper.cpp	~38k	2026-02	C/C++	✅ Very Active
openai/whisper	~79k	2024-12	Python	✅ Active
Assembly AI	N/A (API)	N/A	Cloud API	✅ Active
Deepgram	N/A (API)	N/A	Cloud API	✅ Active

11. MISSING RESEARCH: CLAWDHUB SKILLS

Status: Could not verify ClawdHub URL or existing Google Workspace skills due to rate limiting.

Action Required:

Check ClawdHub documentation/website directly
Search for existing skills:
- google-workspace-*
- gmail-*
- calendar-*
- meeting-*
If no existing skills, create custom skills:
- gogcli-wrapper - Wraps gogcli commands for Clawdbot
- meeting-intelligence - Complete meeting workflow
- google-meet-transcript - Meet-specific transcript processing

Potential Skill Structure:

---
name: google-workspace-meeting-intel
description: "Full Google Workspace meeting intelligence workflow"
tools:
  - gogcli (installed via brew)
  - whisper.cpp (installed locally)
  - claude API (for action items)
---

# Google Workspace Meeting Intelligence

This skill provides:
1. Calendar event monitoring
2. Meeting recording download from Drive
3. Transcript generation via whisper.cpp
4. Action item extraction via Claude
5. Calendar/Tasks update with action items
6. Follow-up email generation

## Commands

### List upcoming meetings
```bash
gog calendar events --days 7 --json

Process meeting recording

./process-meeting.sh <meeting-id>

(Full implementation in skill repository)


---

## 12. NEXT STEPS FOR IMPLEMENTATION

1. **Install gogcli**
   ```bash
   brew install steipete/tap/gogcli
   gog auth credentials ~/Downloads/client_secret.json
   gog auth add your@email.com

Install whisper.cpp

git clone https://github.com/ggml-org/whisper.cpp
cd whisper.cpp
cmake -B build -DGGML_METAL=1  # macOS with Apple Silicon
cmake --build build -j
sh ./models/download-ggml-model.sh turbo

Test Meeting Workflow
- Get Calendar events: gog calendar events --today --json
- Search for recordings: gog drive search "meeting" --json
- Download and transcribe
- Extract action items via Claude API
- Update Calendar/Tasks/Email
Build Clawdbot Skill
- Wrap gogcli commands in skill
- Add whisper.cpp transcription step
- Integrate Claude API for intelligence layer
- Package as reusable automation

QUESTIONS TO CLARIFY

Privacy requirements: Local-only or cloud APIs OK?
Google Workspace: Does client have Workspace or free Gmail?
Meeting platform: Google Meet only or also Zoom/Teams?
Volume: How many meetings/week to process?
Real-time: Need live transcription during meeting or post-processing OK?
Budget: OK with Assembly AI/Deepgram costs (~$0.30-0.50/hr) or local-only?

24 KiB Raw Blame History

Google Workspace + Meeting Intelligence: Tool Research

Executive Summary

1. GOOGLE WORKSPACE CLIs

⭐ gogcli (steipete/gogcli)

google-workspace-cli (ianpatrickhines)

gcalcli (insanum/gcalcli)

2. MODEL CONTEXT PROTOCOL (MCP) SERVERS

⭐ google_workspace_mcp (taylorwilsdon)

3. TRANSCRIPT PROCESSING TOOLS

⭐ whisper.cpp (ggml-org)

openai/whisper (Python)

Assembly AI CLI

Deepgram CLI

4. GOOGLE MEET TRANSCRIPT ACCESS

Google Meet REST API

5. RECOMMENDED ARCHITECTURE

Option A: Local Processing (Privacy-first)

Option B: Cloud Transcription (Production Quality)

Option C: MCP-first (Claude Desktop/Code)

6. CLAWDHUB SKILLS

7. ACTION ITEM EXTRACTION STRATEGIES

Strategy 1: Structured Output with Claude

Strategy 2: Post-Meeting Workflow

8. FINAL RECOMMENDATIONS

For Your Use Case (AI Agent + Meeting Intelligence):

9. ASSEMBLY AI & DEEPGRAM (Cloud Services)

Assembly AI

Deepgram

Cloud vs Local Decision Matrix

10. GITHUB STATS SUMMARY

11. MISSING RESEARCH: CLAWDHUB SKILLS

Process meeting recording

QUESTIONS TO CLARIFY

24 KiB

Raw Blame History