Spark MCP Server

MCP server for accessing Spark Desktop meeting transcripts and emails through the Model Context Protocol.

Features

• 📝 Access all meeting transcripts (calendar-based and ad-hoc)
• 🔍 Full-text search across transcript content
• 📊 Statistics and analytics about your transcripts
• 🔒 Read-only access - safe and non-destructive
• ⚡ Fast local SQLite queries - no network required
• 🎯 Captures ad-hoc meetings (primary use case)

Requirements

• macOS (Spark Desktop must be installed)
• Python 3.10+
• Spark Desktop for macOS (App Store version)

Installation

# Install in development mode
pip install -e .

Usage

With Claude Desktop

Add to your Claude Desktop MCP settings (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "spark": {
      "command": "python",
      "args": ["-m", "spark_mcp.server"],
      "cwd": "/Users/feamster/src/spark-mcp"
    }
  }
}

Or if installed via pip:

{
  "mcpServers": {
    "spark": {
      "command": "spark-mcp"
    }
  }
}

Restart Claude Desktop, and the tools will be available.

Standalone Testing

# Run the server (communicates via stdio)
python -m spark_mcp.server

Available Tools

1. list_meeting_transcripts

List meeting transcripts with metadata.

Parameters:

• limit (optional, default: 20): Max results
• after (optional): Return transcripts with meetingStartDate after this ISO datetime (e.g., '2026-01-30T13:00:00')
• before (optional): Return transcripts with meetingStartDate before this ISO datetime (e.g., '2026-01-30T16:00:00')

Example:

{
  "after": "2026-02-09T09:00:00",
  "before": "2026-02-09T12:00:00",
  "limit": 10
}

Returns:

{
  "transcripts": [
    {
      "messagePk": 63336,
      "subject": "Prior Art Review for Patent Claims 416 and 571",
      "sender": "example@example.com",
      "receivedDate": "2025-11-11 15:59:12",
      "meetingStartDate": "2025-11-11T15:00:00.000Z",
      "meetingEndDate": "2025-11-11T16:00:00.000Z",
      "transcriptId": "-8929133086933914113",
      "isCalendarEvent": false,
      "eventSummary": null,
      "textLength": 29893,
      "hasFullText": true
    }
  ],
  "total": 228
}

2. get_meeting_transcript

Get full transcript content.

Parameters:

• messagePk (optional): Message primary key from list
• transcriptId (optional): Transcript ID (mtid)

Example:

{
  "messagePk": 63336
}

Returns:

{
  "messagePk": 63336,
  "subject": "Prior Art Review for Patent Claims 416 and 571",
  "sender": "example@example.com",
  "recipients": "recipient@example.com",
  "receivedDate": "2025-11-11 15:59:12",
  "meetingStartDate": "2025-11-11T15:00:00.000Z",
  "meetingEndDate": "2025-11-11T16:00:00.000Z",
  "transcriptId": "-8929133086933914113",
  "fullText": "the meeting focused on reviewing prior art for patent claims...",
  "metadata": {
    "language": "auto",
    "status": true,
    "autoProcessed": true,
    "isKept": true,
    "eventSummary": null
  }
}

3. search_meeting_transcripts

Full-text search across transcripts.

Parameters:

• query (required): Search query (FTS5 syntax supported)
• startDate (optional): Filter after this date
• endDate (optional): Filter before this date
• limit (optional, default: 20): Max results
• includeContext (optional, default: true): Include highlighted excerpts

Example:

{
  "query": "neural network AND security",
  "limit": 5
}

FTS5 Query Syntax:

• word1 AND word2 - Both words must be present
• word1 OR word2 - Either word present
• NOT word - Exclude word
• "exact phrase" - Exact phrase match
• word* - Prefix match

Returns:

{
  "results": [
    {
      "messagePk": 62642,
      "subject": "Meeting Summary",
      "sender": "example@example.com",
      "receivedDate": "2025-11-10 23:04:38",
      "excerpt": "...discussing <mark>neural network</mark> architectures for <mark>security</mark> applications...",
      "relevanceScore": 1.5
    }
  ],
  "total": 5
}

4. get_transcript_statistics

Get overview statistics.

Parameters: None

Returns:

{
  "totalTranscripts": 233,
  "calendarMeetings": 37,
  "adHocMeetings": 196,
  "keptTranscripts": 228,
  "deletedTranscripts": 5,
  "withFullText": 225,
  "dateRange": {
    "earliest": "2024-09-01 10:00:00",
    "latest": "2025-11-11 15:59:12"
  },
  "topSenders": [
    {
      "email": "colleague@example.com",
      "count": 45
    }
  ]
}

Data Sources

Databases Used

1. messages.sqlite - Transcript metadata

   • Location: ~/Library/Containers/com.readdle.SparkDesktop.appstore/Data/Library/Application Support/Spark Desktop/core-data/messages.sqlite
   • Tables: messages, meetTranscriptEvent
   • Size: ~178 MB

2. search_fts5.sqlite - Full transcript text

   • Location: ~/Library/Containers/com.readdle.SparkDesktop.appstore/Data/Library/Application Support/Spark Desktop/core-data/search_fts5.sqlite
   • Table: messagesfts (FTS5 full-text index)
   • Size: ~232 MB

Transcript Types

Calendar-Based Meetings (37 transcripts):

• Scheduled meetings with calendar event info
• Stored in meetTranscriptEvent table
• Have eventSummary field

Ad-Hoc Meetings (196 transcripts):

• User-initiated transcriptions
• Not linked to calendar events
• This is the primary use case for most users

Total: 228 kept transcripts (233 including deleted)

Safety Features

• ✅ Read-only database access
• ✅ No writes or modifications
• ✅ Graceful handling of schema changes
• ✅ Safe concurrent access with Spark

Troubleshooting

"Failed to connect to Spark databases"

1. Verify Spark Desktop is installed (App Store version)
2. Check database paths exist:

ls -la ~/Library/Containers/com.readdle.SparkDesktop.appstore/Data/Library/Application\ Support/Spark\ Desktop/core-data/

No transcripts found

• Make sure you have meeting transcripts in Spark
• Check that transcripts are marked as "kept" (not deleted)
• Try running get_transcript_statistics to see counts

Empty transcript text

Some transcripts may not have full text cached locally:

• Recent transcripts may still be syncing
• Deleted transcripts have no content
• Check hasFullText field in list results

Development

# Install in development mode
pip install -e .

# Run tests (if you add them)
pytest

Future Enhancements

See PLAN.md for detailed roadmap, including:

• General email search and processing
• Alternative data access methods (API, IMAP)
• Additional analytics and insights
• Export capabilities

License

MIT