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classifier.dev
dev.classifier/classifier · v1.0.0
Measured by MCP Station
| Last check | Reachable |
|---|---|
| Checked at | — |
| Check success 7 / 30 days | — |
| First-try success 30 days | — |
| Median response | — |
| Lookups 30 days | — |
| Authentication | No auth |
| MCP version | 2025-06-18 |
| Rank score | — |
| Declared risk | Can write (some tools are not declared read-only) |
| Capabilities declared | tools |
| Tool definitions | 5 tools · with description 100% · input schema is an object 100% · read/destructive hints 100% |
Description written by the publisher, not verified
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
claude mcp add --transport http classifier https://classifier.dev/mcp{
"mcpServers": {
"classifier": {
"url": "https://classifier.dev/mcp"
}
}
}Tools as reported by the server
classify_textsSort 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 fewerclassify_dimensionsClassify 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_labelLike 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_labelsClassify 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 answerreview_uncertainClassify 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
Repository · Website · JSON · Text for agents · Registry updated 2026-09-19