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Findings on what compounds and supplements do in the body, with evidence strength, quote and paper.
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classifier.dev
dev.classifier/classifier
classifier.devVerified
Sort up to 1,000 texts into your own labels with a calibrated confidence per answer. No API key.
OtherIs classifier.dev down?Alternatives
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Responded
The server completed its last measured handshake. This is an observation, not a guarantee.
Median latency
618 ms
Uptime, 30 days
Not measured
Last checked
Measurement details
Core
- Pass
- Pass
- Pass
- Pass
- Pass
Performance
- Not measured
- Not measured
- Not measured
- Pass
Access
- Pass
- Not measured
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Tools
- Pass
- Pass
- Pass
- Pass
Publisher
- Pass
- Pass
- Pass
- Not measured
- Pass
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Tools
- 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.
Measured
- 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.
Measured
- 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.
Measured
- 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.
Measured
- 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.
Measured
Probe history
- ok578 ms5 tools
- ok658 ms5 tools
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Connect to this server
Claude Code
claude mcp add --transport http classifier-dev https://classifier.dev/mcpCursor .cursor/mcp.json
{
"mcpServers": {
"classifier-dev": {
"url": "https://classifier.dev/mcp"
}
}
}VS Code .vscode/mcp.json
{
"servers": {
"classifier-dev": {
"type": "http",
"url": "https://classifier.dev/mcp"
}
}
}