classifier.dev

dev.classifier/classifierVersion 1.0.0

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

Status

Responds234 msNov 14, 2026, 14:26 UTCAnswered our handshake without sign-in.

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Last checked: Nov 14, 2026, 14:26 UTC
Latency: 234 ms
Remotes responding: 1 of 1
Sign-in: No sign-in
Hosting: Hosted outside the EU (detected)
Registry data as of: Nov 14, 2026, 21:12 UTC

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Connect

Streamable HTTP

https://classifier.dev/mcpResponds234 msNov 14, 2026, 14:26 UTC

No sign-in

Claude Code

claude mcp add --transport http classifier https://classifier.dev/mcp

Claude Desktop and claude.ai

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Settings > Connectors > Add custom connector
Name: classifier
Remote MCP server URL: https://classifier.dev/mcp

Cursor

{
  "mcpServers": {
    "classifier": {
      "url": "https://classifier.dev/mcp"
    }
  }
}

VS Code

code --add-mcp '{"name":"classifier","type":"http","url":"https://classifier.dev/mcp"}'

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Tools

5 tools

Tools of classifier.dev
	Description		Read-only	Idempotent	Open world
`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 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.	No	No	No	Yes
`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.	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.	No	No	No	Yes
`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.	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.	No	No	No	Yes
`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.	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.	No	No	No	Yes
`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.	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.	No	No	No	Yes

Details

Registry status: Active
Published: Sep 19, 2026
Updated: Sep 19, 2026
Website: https://classifier.dev/⁠ (opens in a new tab)
Repository: https://github.com/mrmps/classifier-dev⁠ (opens in a new tab)
Categories: Uncategorized
GDPR: Not declared

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