MCP server

classifier.dev Official

dev.classifier/classifier

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

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

Website Source

At a glance

Type

MCP server

Publisher

classifier.dev (verified by the MCP Registry)

Status

Working at last check

Tools

5

Token cost

About 3,823 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

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

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.classifier]
url = "https://classifier.dev/mcp"

Gemini CLI

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

Cursor

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

VS Code

{"servers":{"classifier":{"type":"http","url":"https://classifier.dev/mcp"}}}

Antigravity

agy mcp add classifier https://classifier.dev/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"classifier","server_url":"https://classifier.dev/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"classifier","server_url":"https://classifier.dev/mcp"}

Tools

• classify_dimensions ~925 tokens
  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_multi_label ~762 tokens
  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 >
• classify_texts ~1114 tokens
  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
• count_labels ~514 tokens
  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 answer
• review_uncertain ~508 tokens
  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. Re

Related MCP servers

• EventTrader MCP
  AI prediction market trading — real-time data, CLOB exchange, Prop Desk, 40+ tools
• classifier.dev docs
  classifier.dev documentation as tools: list, read and search the reference, benchmark and guides.
• Brainiall NLP
  Sentiment, toxicity, entity extraction, PII, translation, summary, QA, fraud scoring, safety audit.
• SlideForge
  Deterministic, fully editable PowerPoint from typed slide intents. 200+ layouts, brand templates.
• Tanod ML
  Local ML: speech to text (SRT/VTT), text embeddings, reranking, similarity, entities, zero-shot.
• Micro Mu
  News, web search, mail, markets, weather, places, files, calendar, contacts. 67 tools, one endpoint.
• Hunch
  Calibrated judgments for text: yes/no probabilities, picks from your options, or scores.
• Kaneme — Your writing identity, measured and portable
  Build a voice from your own texts, measure whether a text still sounds like you, then write in it.

Badge for your README

[![AptLynx](https://aptlynx.net/badge/dev.classifier/classifier.svg)](https://aptlynx.net/servers/dev.classifier/classifier)

Listing text is written by the maker; AptLynx lists tools and does not vouch for them. Find another tool · Browse by topic · For agents