/add-agents-platform | elevenlabs

🚨 EXECUTION NOTICE FOR CLAUDE

When you invoke this command via SlashCommand, the system returns THESE INSTRUCTIONS below.

YOU are the executor. This is NOT an autonomous subprocess.

✅ The phases below are YOUR execution checklist
✅ YOU must run each phase immediately using tools (Bash, Read, Write, Edit, TodoWrite)
✅ Complete ALL phases before considering this command done
❌ DON't wait for "the command to complete" - YOU complete it by executing the phases
❌ DON't treat this as status output - it IS your instruction set

Immediately after SlashCommand returns, start executing Phase 0, then Phase 1, etc.

See @CLAUDE.md section "SlashCommand Execution - YOU Are The Executor" for detailed explanation.

Available Skills

This commands has access to the following skills from the elevenlabs plugin:

api-authentication: API authentication patterns, SDK installation scripts, environment variable management, and connection testing for ElevenLabs. Use when setting up ElevenLabs authentication, installing ElevenLabs SDK, configuring API keys, testing ElevenLabs connection, or when user mentions ElevenLabs authentication, xi-api-key, ELEVENLABS_API_KEY, or ElevenLabs setup.
mcp-integration
production-deployment: Production deployment patterns for ElevenLabs API including rate limiting, error handling, monitoring, and testing. Use when deploying to production, implementing rate limiting, setting up monitoring, handling errors, testing concurrency, or when user mentions production deployment, rate limits, error handling, monitoring, ElevenLabs production.
stt-integration: ElevenLabs Speech-to-Text transcription workflows with Scribe v1 supporting 99 languages, speaker diarization, and Vercel AI SDK integration. Use when implementing audio transcription, building STT features, integrating speech-to-text, setting up Vercel AI SDK with ElevenLabs, or when user mentions transcription, STT, Scribe v1, audio-to-text, speaker diarization, or multi-language transcription.
tts-integration
vercel-ai-patterns
voice-processing: Voice cloning workflows, voice library management, audio format conversion, and voice settings. Use when cloning voices, managing voice libraries, processing audio for voice creation, configuring voice settings, or when user mentions voice cloning, instant cloning, professional cloning, voice library, audio processing, voice settings, or ElevenLabs voices.

To use a skill:

!{skill skill-name}

Use skills when you need:

Domain-specific templates and examples
Validation scripts and automation
Best practices and patterns
Configuration generators

Skills provide pre-built resources to accelerate your work.

Security Requirements

CRITICAL: All generated files must follow security rules:

@docs/security/SECURITY-RULES.md

Key requirements:

Never hardcode API keys or secrets
Use placeholders: your_service_key_here
Protect .env files with .gitignore
Create .env.example with placeholders only
Document key acquisition for users

Arguments: $ARGUMENTS

Goal: Add ElevenLabs Agents Platform with full MCP integration, enabling conversational voice agents that can access external tools via Model Context Protocol.

Core Principles:

Build conversational voice agents with natural dialogue
Integrate MCP servers (Zapier, custom servers)
Configure fine-grained security controls for tool access
Support real-time WebSocket conversations
Enable multi-turn voice dialogues

Phase 1: Discovery Goal: Understand agents and MCP requirements

Actions:

Load SDK documentation: @elevenlabs-documentation.md
Load MCP examples: @docs/elevenlabs/docs/mcp-integration-examples.md
Check setup: !{bash npm list @elevenlabs/elevenlabs-js 2>/dev/null || pip show elevenlabs 2>/dev/null}
Detect framework: @package.json or @pyproject.toml
Parse $ARGUMENTS for agent and MCP needs

Phase 2: Requirements Gathering Goal: Clarify agent capabilities

Actions:

Use AskUserQuestion if needed:
- Agent purpose? (customer service, personal assistant, sales, etc.)
- MCP servers? (Zapier, custom, none)
- Tool approval mode? (always ask, fine-grained, auto-approve)
- Real-time conversations? (WebSocket streaming)
- Multi-agent system? (specialized agents for different tasks)

Phase 3: Planning Goal: Design agents platform integration

Actions:

Plan implementation:
- Agent configuration: name, description, voice, model
- MCP server setup: URLs, auth tokens, approval modes
- Tool definitions: allowed, approval-required, disabled
- WebSocket connection for real-time conversations
- Conversation management: sessions, state, history
- Security: tool approval workflow, user confirmation
Present plan

Phase 4: Implementation Goal: Build agents platform with MCP

Actions:

Launch the elevenlabs-agents-builder agent to implement conversational agents with MCP.

Provide detailed requirements:

Context: Framework, SDK status, MCP needs
Target: $ARGUMENTS
Requirements:
- Agent Configuration:
  - Create agent with voice_id and model selection
  - Set agent personality and instructions
  - Configure conversation parameters
- MCP Integration (MAJOR FEATURE):
  - Configure MCP server URLs
  - Set authentication tokens (from environment)
  - Define tool approval modes:
    - always_ask: request permission for each tool
    - fine_grained: custom rules per tool
    - auto_approve: allow without asking (read-only tools)
    - disabled: block specific tools
  - Implement approval request handling
  - Examples: Zapier MCP (hundreds of tools), custom MCP servers
- Real-time Conversations:
  - WebSocket connection setup
  - Multi-turn dialogue handling
  - Conversation state management
  - Voice streaming for agent responses
- Tool Calling:
  - Define available tools from MCP
  - Handle tool execution requests
  - Process tool responses
  - Integrate results into conversation
- Security:
  - Tool approval workflow UI
  - User confirmation for sensitive operations
  - Rate limiting for tool calls
  - Audit logging
- Use progressive docs: fetch agents platform and MCP docs
Expected output:
- Agent configuration files/code
- MCP server integration
- WebSocket conversation client
- Tool approval interface
- Example conversations

Phase 5: Verification Goal: Ensure agents work correctly

Actions:

Verify files created
Check syntax: !{bash npx tsc --noEmit 2>/dev/null || python -m py_compile *.py 2>/dev/null}
Verify MCP configuration
Test WebSocket connection setup

Phase 6: Summary Goal: Guide on using agents platform

Actions:

Display summary:
- Agent created: [name and purpose]
- MCP servers configured: [list]
- Tools available: [count]
- Security mode: [approval configuration]
Usage instructions:
- Start conversation with agent
- Agent can use MCP tools (with approval)
- Multi-turn dialogue examples
- Tool approval workflow
Configuration:
- MCP dashboard: https://elevenlabs.io/app/agents/integrations
- Zapier MCP: https://zapier.com/mcp
- Custom MCP servers
Show code examples
Next steps:
- Add more MCP servers
- Customize tool permissions
- Build multi-agent systems
- Production deploy: /elevenlabs:add-production