| name | ai-orchestration |
| description | Multi-model AI collaboration via orchestrator MCP. Use when seeking second opinions, debugging complex issues, building consensus on architectural decisions, conducting code reviews, or needing external validation on analysis. |
| triggers | second opinion, multi-model, consensus, external AI, codex, gemini, model comparison, AI collaboration, expert validation, parallel AI, ai_spawn, ai_fetch |
AI CLI Orchestration
Query external AI models (claude, codex, gemini) for second opinions, debugging, consensus building, and expert validation.
Tools Overview
| Tool | Mode | Description |
|---|
ai_call | Synchronous | Call AI and wait for result |
ai_spawn | Async | Start AI in background, get job ID |
ai_fetch | Async | Get result from spawned AI (with timeout) |
ai_list | Utility | List all running/completed AI jobs |
ai_review | Convenience | Spawn all 3 AIs in parallel with same prompt |
Role Hierarchy
| CLI | Role | Mode | Capabilities |
|---|
| claude | Worker/Peer | Full | Can execute any tool/command |
| codex | Reviewer | Read-only | Code review, analysis, suggestions |
| gemini | Researcher | Read-only | Web search, documentation lookup |
Parallel Execution (Recommended)
claude_job = ai_spawn(cli="claude", prompt="Analyze this code for bugs...")
codex_job = ai_spawn(cli="codex", prompt="Review this code for patterns...")
gemini_job = ai_spawn(cli="gemini", prompt="Research best practices for...")
claude_result = ai_fetch(job_id=claude_job.job_id, timeout=120)
codex_result = ai_fetch(job_id=codex_job.job_id, timeout=120)
gemini_result = ai_fetch(job_id=gemini_job.job_id, timeout=120)
Or use ai_review for convenience:
review = ai_review(prompt="Analyze this architecture decision...", files=["src/"])
claude_result = ai_fetch(job_id=review.jobs["claude"].job_id, timeout=120)
When to Use External Models
Do use when: Stuck on complex bugs, architectural decisions with tradeoffs, need validation before major refactoring, security-sensitive code, want diverse perspectives
Don't use when: Simple work, already confident, just executing known solution
References
Tips
- Use parallel for multi-model:
ai_spawn + ai_fetch is 3x faster than sequential
- Be specific: Include file paths, error messages, and context
- Use appropriate CLI: codex for code review, gemini for web search
- Delegate complex work: Use sub-agents for structured analysis
- Remember read-only: Codex and Gemini cannot execute commands or modify files
- Include files: Use the
files parameter to provide code context
- Monitor jobs: Use
ai_list() to check status of all running jobs
