INDIVIDUAL MCP TOOL

review_uncertain

Classify up to 1,000 texts and return only the ones whose confidence fell under a threshold (default 0.

classifier.devnone authenticationAvailability not checked

LIVE ENDPOINT

https://classifier.dev/mcp

No auth detected

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

{}

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.

CONNECT WITH APPROVAL

Review this server and its permissions before adding it. Secret placeholders must be set locally.

Codex

~/.codex/config.toml

[mcp_servers.classifier-dev]
url = "https://classifier.dev/mcp"
enabled = true

Claude Code

.mcp.json

{
  "mcpServers": {
    "classifier-dev": {
      "type": "http",
      "url": "https://classifier.dev/mcp"
    }
  }
}

Claude Desktop

Settings → Connectors → Add custom connector

Name: classifier-dev
Remote MCP URL: https://classifier.dev/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": {
    "classifier-dev": {
      "url": "https://classifier.dev/mcp"
    }
  }
}

Visual Studio Code

.vscode/mcp.json

Add to Visual Studio Code

{
  "servers": {
    "classifier-dev": {
      "type": "http",
      "url": "https://classifier.dev/mcp"
    }
  }
}

Generic MCP

Client-specific MCP configuration

{
  "name": "classifier-dev",
  "transport": "streamable-http",
  "url": "https://classifier.dev/mcp"
}

MCP Inspector

Run the official MCP Inspector locally and enter the indexed Streamable HTTP endpoint.

classify_texts — Sort up to 1,000 texts into exactly one of your own labels each, with a calibrated confidence per answer.
classify_multi_label — Like classify_texts, but each text gets every label that applies (possibly none), with an independent 0-1 score per label.
count_labels — Classify up to 1,000 texts and return only a histogram: how many landed on each label, and how many the model was unsure about.