Switchback MCP
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Switchback MCPrecommend a model for fixing a typo in README"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Switchback MCP
MCP server for Switchback — classify AI agent turns by complexity and pick the cheapest model that can handle them. Drop-in for Claude Desktop, Cursor, Cline, and any MCP client.
Built by VibeKit — same cascade router that powers every "Auto" turn in our hosted product. Learn more →
What it does
Exposes two tools over the Model Context Protocol:
classify_turn— given a user message, returns{tier: 0|1|2, why, fallback, durationMs}. Use it when you're deciding whether to spend on a flagship model or stay on a cheap one for the next agent step.recommend_model— given a user message + a ladder of models (cheap → flagship), returns the cheapest model on the ladder that can plausibly handle it. Convenience wrapper aroundclassify_turn.
Both tools fire a single small-model call via OpenRouter (default: openai/gpt-5.4-mini, ~$0.0001/call). Fail-soft: any error returns tier 1 with fallback: true rather than blocking your agent.
Related MCP server: oracle-models
Install
npm install -g vibekit-switchback-mcpConfigure
Set your OpenRouter key (get one at https://openrouter.ai/keys):
export OPENROUTER_API_KEY=sk-or-...Optional: override the classifier model:
export SWITCHBACK_CLASSIFIER_MODEL=anthropic/claude-haiku-4.5Claude Desktop
Add to your ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"switchback": {
"command": "npx",
"args": ["-y", "vibekit-switchback-mcp"],
"env": {
"OPENROUTER_API_KEY": "sk-or-..."
}
}
}
}Cursor / Cline / others
Any MCP client that supports stdio servers. Same npx invocation.
Example calls
classify_turn:
{
"userMessage": "rebuild the auth flow with passkeys"
}returns:
{
"tier": 2,
"why": "architectural rework + new auth method",
"fallback": false,
"durationMs": 412
}recommend_model:
{
"userMessage": "fix typo in README",
"ladder": [
"openai/gpt-5.4-mini",
"openai/gpt-5.4",
"openai/gpt-5.5"
]
}returns:
{
"recommended_model": "openai/gpt-5.4-mini",
"tier": 0,
"ladder_size": 3,
"classifier": { "tier": 0, "why": "trivial copy edit", "fallback": false, "durationMs": 318 }
}When to use it
Multi-step agents where you can swap models between rounds.
BYOK-style products where you want to keep all routing inside the user's chosen brand — pass a brand-locked ladder per user.
Cost-conscious orchestrators that want to avoid spending flagship rates on trivial turns.
When NOT to use it
One-shot chat completions where you can't change models after the first call — use OpenRouter's
autoor NotDiamond/Martian instead.Latency-critical sub-200ms-first-token UX — the classifier adds 200-500ms.
License
MIT. See LICENSE.
Built by VibeKit. The core library is vibekit-switchback — this package is the MCP wrapper.
This server cannot be deployed
Maintenance
Related MCP Connectors
Cost-optimized LLM model routing recommendations for autonomous AI agents
Reduces AI Agent token usage by 40% via three-stage SOP workflow.
SaaS intelligence for AI agents. 5 unified tools cover 1,000+ services with 91-96% token savings.
AI agent infrastructure: dedup, cost prediction, validation, governance, failure intelligence.
Related MCP Servers
- AlicenseNot gradedqualityDmaintenanceA model routing advisor for autonomous agents — get cost-optimised LLM recommendations via MCP.4 npmMIT
- AlicenseAqualityDmaintenanceClassifies development task complexity (LIGHT/MEDIUM/HEAVY) and recommends the most cost-efficient AI model per provider, enabling optimized model selection for coding tasks.317 npmMIT
- AlicenseAqualityDmaintenanceOptimizes token costs by intelligently delegating low-complexity tasks to local LLMs via LiteLLM, enabling cost-effective development workflows.31MIT
- AlicenseAqualityCmaintenancePre-execution cost estimation for LLM agent workflows, providing cost estimates before running tasks and improving accuracy over time through calibration.62MIT