Connect Claude, Cursor, Windsurf, and other AI assistants to your Speak AI workspace.
83 tools, 5 resources, 3 prompts, 28 CLI commands — transcribe, analyze, search, and manage media at scale.

1. Go to Speak AI API Keys
2. Copy your API Key

Detects installed MCP clients and configures them automatically:

npm install -g @speakai/mcp-server
speakai-mcp init

{
  "mcpServers": {
    "speak-ai": {
      "command": "npx",
      "args": ["-y", "@speakai/mcp-server"],
      "env": {
        "SPEAK_API_KEY": "your-api-key"
      }
    }
  }
}

export SPEAK_API_KEY="your-api-key"
claude mcp add speak-ai -- npx -y @speakai/mcp-server

{
  "mcpServers": {
    "speak-ai": {
      "command": "npx",
      "args": ["-y", "@speakai/mcp-server"],
      "env": {
        "SPEAK_API_KEY": "your-api-key"
      }
    }
  }
}

{
  "mcpServers": {
    "speak-ai": {
      "command": "npx",
      "args": ["-y", "@speakai/mcp-server"],
      "env": {
        "SPEAK_API_KEY": "your-api-key"
      }
    }
  }
}

{
  "mcpServers": {
    "speak-ai": {
      "command": "npx",
      "args": ["-y", "@speakai/mcp-server"],
      "env": {
        "SPEAK_API_KEY": "your-api-key"
      }
    }
  }
}

SPEAK_API_KEY=your-key npx @speakai/mcp-server

Resources provide direct data access without tool calls. Clients can read these URIs directly.

Pre-built workflow prompts that agents can invoke to run multi-step tasks.

Upload a recording and get a full analysis — transcript, insights, action items, and key takeaways.

Parameters: url (required), name (optional)

Example: "Use the analyze-meeting prompt with url=https://example.com/standup.mp3"

Search for themes, patterns, or topics across multiple recordings or your entire library.

Parameters: topic (required), folder (optional)

Example: "Use the research-across-media prompt with topic='customer churn reasons'"

Prepare a brief from recent meetings — pull transcripts, extract decisions, and summarize open items.

Parameters: days (optional, default: 7), folder (optional)

Example: "Use the meeting-brief prompt with days=14 to cover the last two weeks"

Install globally and configure once:

npm install -g @speakai/mcp-server
speakai-mcp config set-key

Or run without installing:

npx @speakai/mcp-server config set-key

Every command supports:

• --json — output raw JSON (for scripting and piping)
• --help — show command-specific help

# Upload and wait for processing
speakai-mcp upload https://example.com/interview.mp3 -n "Q1 Interview" --wait

# Upload a local file
speakai-mcp upload ./meeting-recording.mp4

# Get plain-text transcript
speakai-mcp transcript abc123 --plain > meeting.txt

# Export as PDF with speaker names
speakai-mcp export abc123 -f pdf --speakers

# Ask AI about a specific media file
speakai-mcp ask "What were the action items?" -m abc123

# Ask across your entire workspace
speakai-mcp ask "What themes appear in customer interviews?"

# Search all transcripts
speakai-mcp search "pricing concerns" --from 2026-01-01

# Create a text note from stdin
cat notes.txt | speakai-mcp create-text "Meeting Notes"

# Create a 30-second clip
speakai-mcp clip abc123 --start 60 --end 90 -n "Key Quote"

# Schedule a meeting bot
speakai-mcp schedule-meeting "https://zoom.us/j/123456" -t "Weekly Standup"

# List videos as JSON for scripting
speakai-mcp ls --type video --json | jq '.mediaList[].name'

# List media from the last week
speakai-mcp ls --from 2026-03-19 --to 2026-03-26

# Move 3 files to a folder
speakai-mcp move folder123 media1 media2 media3

You: "Upload and transcribe this recording: https://example.com/meeting.mp3"

AI: I'll upload and analyze that for you.
    -> upload_and_analyze(url, name)
    [waits for processing... returns transcript + insights]

    Here's your transcript with 3 speakers identified.
    Key insights: 5 action items, positive sentiment overall.
    Top topics: Q2 roadmap, hiring plan, budget review.

You: "What themes came up across all our customer interviews this month?"

AI: Let me search your media library.
    -> search_media(query: "customer interview", startDate: "2026-03-01")
    -> ask_magic_prompt(mediaIds: [...], prompt: "What are the recurring themes?")

    Across 12 interviews, the top themes were:
    1. Pricing sensitivity (mentioned in 8/12)
    2. Onboarding friction (6/12)
    3. Feature requests for API access (5/12)

You: "Join my 2pm Zoom call, then send me a summary with action items"

AI: -> schedule_meeting_event(meetingUrl, scheduledAt: "2026-03-25T14:00:00Z")

    Done. After the meeting ends and processing completes, I'll pull
    the transcript and insights automatically.

    [After the meeting]
    -> get_media_insights(mediaId)
    -> ask_magic_prompt(mediaIds: [...], prompt: "List all action items with owners")

    Here's your meeting summary with 7 action items...

You: "Prepare a brief from all meetings in the last week"

AI: -> list_media(from: "2026-03-18", mediaType: "audio")
    -> get_media_insights(mediaId) [for each of 5 meetings]

    Weekly Meeting Brief (Mar 18-25):
    - Engineering Standup: Deployed v2.3, 2 bugs triaged
    - Sales Review: Pipeline at $1.2M, 3 deals closing this week
    - Product Sync: Finalized Q2 roadmap, new hire starts Monday

    Consolidated Action Items: [12 items grouped by owner]

The MCP server and CLI handle token management automatically. If you're calling the REST API directly, here's the full auth flow:

Step 1 — Get an access token:

curl -X POST https://api.speakai.co/v1/auth/accessToken \
  -H "Content-Type: application/json" \
  -H "x-speakai-key: YOUR_API_KEY"

Response:

{
  "data": {
    "email": "you@example.com",
    "accessToken": "eyJhbG...",
    "refreshToken": "eyJhbG..."
  }
}

Step 2 — Use the token on all subsequent requests:

curl https://api.speakai.co/v1/media \
  -H "x-speakai-key: YOUR_API_KEY" \
  -H "x-access-token: ACCESS_TOKEN_FROM_STEP_1"

Step 3 — Refresh before expiry:

curl -X POST https://api.speakai.co/v1/auth/refreshToken \
  -H "Content-Type: application/json" \
  -H "x-speakai-key: YOUR_API_KEY" \
  -H "x-access-token: CURRENT_ACCESS_TOKEN" \
  -d '{"refreshToken": "REFRESH_TOKEN_FROM_STEP_1"}'

Token Lifetimes:

Auth Rate Limits: 5 requests per 60 seconds on both /v1/auth/accessToken and /v1/auth/refreshToken.

• Folder IDs: Folders have both _id (MongoDB ObjectId) and folderId (string). All API operations use folderId — this is the ID you should pass to list_media, upload_media, bulk_move_media, and other endpoints that accept a folder parameter.
• Media IDs: Media items use mediaId (returned in list responses as _id).

• The MCP client automatically retries on 429 with exponential backoff
• For direct API usage, implement exponential backoff and respect Retry-After headers
• Cache stable data (folder lists, field definitions, supported languages)
• Use export_multiple_media over individual exports for batch operations
• Use upload_and_analyze instead of manual upload + poll + fetch loops
• Use bulk_move_media to move multiple items at once instead of updating one by one
• Use bulk_update_transcript_speakers to rename speakers across many files instead of calling update_transcript_speakers per file
• Use list_media with include: ["transcription"] to fetch media with transcripts inline, avoiding N+1 calls to get_transcript

All tool errors follow this structure:

{
  "content": [{ "type": "text", "text": "Error: HTTP 401: {\"message\": \"Invalid API key\"}" }],
  "isError": true
}

git clone https://github.com/speakai/speakai-mcp.git
cd speakai-mcp

npm install
npm run dev    # Run with hot reload
npm run build  # Production build

• Speak AI — Platform
• API Documentation — Full API reference
• MCP Protocol — Model Context Protocol spec
• npm Package — npm registry
• Support — Email us

MIT