← huggingbay.xyz

INDIVIDUAL MCP TOOL

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.

huggingbay.xyznone authenticationAvailability not checked

LIVE ENDPOINT

https://bay-run-mvp-zfmlsu2yla-uc.a.run.app/mcp

No auth detected

Connect to this endpoint to inspect the live schema for classify and invoke it with your own arguments.

Indexed input schema

{}

Risk classification

Inferred read-only · medium confidence · heuristic, not a guarantee.

  • No write-capable action terms were found; this is not proof that invocation has no side effects.

Parent server

huggingbay.xyz

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

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  • 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.
  • 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.
  • request_specialist — Ask Bay Run for a specialist for a task — and NEVER get a dead end.