INDIVIDUAL MCP TOOL
route
RUNTIME auto-router: send a job WITHOUT knowing which specialist to use, and Bay Run picks the best mirrored specialist per-request at inference time.
LIVE ENDPOINT
https://bay-run-mvp-zfmlsu2yla-uc.a.run.app/mcp
Connect to this endpoint to inspect the live schema for route and invoke it with your own arguments.
Indexed input schema
{}Risk classification
Potential side effects detected · medium confidence · heuristic, not a guarantee.
- A tool name or description contains a write-action term.
- A tool name or description suggests sending messages.
Parent server
CONNECT WITH APPROVAL
Client installation
Review this server and its permissions before adding it. Secret placeholders must be set locally.
Codex
~/.codex/config.toml
[mcp_servers.bay-run]
url = "https://bay-run-mvp-zfmlsu2yla-uc.a.run.app/mcp"
enabled = true
Claude Code
.mcp.json
{
"mcpServers": {
"bay-run": {
"type": "http",
"url": "https://bay-run-mvp-zfmlsu2yla-uc.a.run.app/mcp"
}
}
}
Claude Desktop
Settings → Connectors → Add custom connector
Name: bay-run
Remote MCP URL: https://bay-run-mvp-zfmlsu2yla-uc.a.run.app/mcp
Add this remote URL as a custom connector in Claude Desktop. Availability depends on the user plan and workspace policy.
Cursor
.cursor/mcp.json
{
"mcpServers": {
"bay-run": {
"url": "https://bay-run-mvp-zfmlsu2yla-uc.a.run.app/mcp"
}
}
}
Visual Studio Code
.vscode/mcp.json
Add to Visual Studio Code{
"servers": {
"bay-run": {
"type": "http",
"url": "https://bay-run-mvp-zfmlsu2yla-uc.a.run.app/mcp"
}
}
}
Generic MCP
Client-specific MCP configuration
{
"name": "bay-run",
"transport": "streamable-http",
"url": "https://bay-run-mvp-zfmlsu2yla-uc.a.run.app/mcp"
}
MCP Inspector
Run the official MCP Inspector locally and enter the indexed Streamable HTTP endpoint.
Related tools
discover_models— Search a 147K-model catalog for a small, cheap, open specialist model that does ONE narrow job better/cheaper than a general LLM — embeddings, reranking, text classification, NER/extraction, routing, guardrails, transcription, vision, audio.eval_models— Prove which candidate model actually wins on YOUR data before committing — a head-to-head bake-off, not a public leaderboard (MTEB rank does NOT predict your-domain performance).embed— Turn text into embedding vectors using ANY open embedding model, served instantly on demand — no packaging, no deploy, no GPU.rerank— Reorder candidate documents by true relevance to a query using an open cross-encoder/reranker, served instantly on demand — the standard move to sharpen RAG / search precision after a vector search returns a noisy top-k.extract— Turn messy HTML/text (e.find_specialist_for_task— ONE call to find the best small specialist model for your task, proven on YOUR examples.classify— Classify text with a small CPU-served text-classification specialist — the guardrail / safety / moderation / sentiment / intent / NLI layer agents need and that frontier routers don't offer as tiny models.request_specialist— Ask Bay Run for a specialist for a task — and NEVER get a dead end.