Official MCP RegistryListed
classifier.dev
Sort up to 1,000 texts into your own labels with a calibrated confidence per answer. No API key.
First seen 2 Oct 2026. Evidence as of 5 Oct 2026.
5
Tools
From an anonymous probe
1
Source listings
Each with its own history
10
Recorded changes
Since first seen
Tools
| Tool | Description | Behaviour |
|---|---|---|
| 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. | Changes data |
| 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 >= 0.7 are kept. | Changes data |
| 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 than about five items you can already see — just decide. For default Jev, confidence is calibrated (answers >= 0.9 are right ~82-92% of the time; < 0.5 about 30-60%); these measurements do not apply to experimental Laya. so act on the sure ones and look at the rest yourself, or pass tier "smart" to have the unsure ones re-asked of a reasoning model. | Changes data |
| 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 answers into context. Use classify_texts when you need the answer per item. | Changes data |
| 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. Returns the index of each item so you can map back to your list. | Changes data |
Change history
- review_uncertain: input schema changed (+share_data)
- review_uncertain: annotations changed
- count_labels: input schema changed (+share_data)
- count_labels: annotations changed
- classify_texts: input schema changed (+share_data)
- classify_texts: annotations changed
- classify_multi_label: input schema changed (+share_data)
- classify_multi_label: annotations changed
- classify_dimensions: input schema changed (+share_data)
- classify_dimensions: annotations changed
| Source | Listing | First seen | Last seen | Versions |
|---|---|---|---|---|
| Official MCP Registry | dev.classifier/classifier | 2 Oct 2026 | 5 Oct 2026 | 1 |