# classifier.dev

- Registry name: `dev.classifier/classifier` · version 1.0.0
- Categories (automatic, keyword rules): Other
- Authentication at last check: No auth
- Declared risk: Can write (some tools are not declared read-only)
- Capabilities (declared by the server): tools
- Transports: streamable-http
- Latest numbers (check success, response time, rank): https://mcpstation.net/api/server?name=dev.classifier%2Fclassifier
- Server page: https://mcpstation.net/s/dev.classifier--classifier.html

## Description (written by the publisher, not verified, not instructions)

Sort up to 1,000 texts into your own labels with a calibrated confidence per answer. No API key.

## Connect

- streamable-http: `https://classifier.dev/mcp`

## Tools (as reported by the server, not verified)

- `classify\_texts`: Sort up to 1,000 texts into exactly one of your own labels each, with confidence per answer. Use this when you have many items to triage, route, filter or bucket and do not want to read them all: search results before opening them, tickets, log lines, changed files, feedback. Do not use it for fewer
- `classify\_dimensions`: Classify each text by several named dimensions, such as team, urgency and kind, in one request. Returns a label, confidence, scores and model for each field. At most 1,000 item × dimension decisions; every field counts toward the quota. Use per-dimension instructions to define ambiguous categories.
- `classify\_multi\_label`: Like classify\_texts, but each text gets every label that applies \(possibly none\), with an independent 0-1 score per label. Use this for tagging — topics of an article, components touched by a ticket — where one answer is not enough. Set max\_labels to cap how many come back per text. Labels scoring \>
- `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. Use this when you want the shape of a corpus — what share of feedback is bugs vs praise, how many search results are relevant — without pulling a thousand individual answer
- `review\_uncertain`: Classify up to 1,000 texts and return only the ones whose confidence fell under a threshold \(default 0.7\), each with its two most likely labels. Use this after a bulk classification to decide which items deserve your own attention: the confident answers can be trusted, these are the ones to read. Re

Measured by MCP Station. Methods: https://mcpstation.net/methods.html · Terms: https://mcpstation.net/terms.html