classifier.dev
Sort up to 1,000 texts into your own labels with a calibrated confidence per answer. No API key.
| Endpoint | https://classifier.dev/mcp (streamable-http) |
|---|---|
| Registry version | 1.0.0 |
| Now | 5 tools · grade F (45) · pin ts_17d8ac3f06e0317ee3635687445c09d9 · protocol 2025-06-18 |
| First / last listed | 2026-11-01 02:32 UTC / 2026-11-01 02:32 UTC |
| Grade over time | 2026-11-01: F (45), 5 tools |
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Tools: classify_texts classify_dimensions classify_multi_label count_labels review_uncertain
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 params: share_data, url, include, inputs, labels*, instructions, tier, model, processing |
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. params: share_data, url, include, items, dimensions*, instructions, tier, model, processing |
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. params: url, include, share_data, inputs, labels*, instructions, max_labels, model, processing |
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. params: share_data, inputs*, labels*, instructions, unsure_below |
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. params: share_data, inputs*, labels*, instructions, below |
Entries describe changes in tool definitions. They are not statements about a server, its quality or its author. The grade measures how the tool definitions fit model providers (token cost, schema compatibility, confusable names) as scored by the ToolFit engine at the time of the listing. JSON: append .json. Opt-out: how or hello@youraitools.co.