classifier.dev

{
  "protocolVersion": "0.3.0",
  "name": "classifier.dev",
  "description": "Zero-shot text classification over plain HTTP. Free without a key; paid plans raise rate limits 10-100x. Sort up to 1,000 texts into your own labels in one call with a calibrated confidence per answer. This service's live agent interface is MCP (Streamable HTTP) at the URL below; A2A message/send is not implemented.",
  "url": "https://classifier.dev/mcp",
  "preferredTransport": "JSONRPC",
  "additionalInterfaces": [
    {
      "url": "https://classifier.dev/mcp",
      "transport": "JSONRPC",
      "protocol": "mcp"
    },
    {
      "url": "https://classifier.dev/",
      "transport": "HTTP+JSON",
      "protocol": "rest"
    }
  ],
  "version": "1.0.0",
  "provider": {
    "organization": "Michael Ryaboy",
    "url": "https://classifier.dev"
  },
  "documentationUrl": "https://classifier.dev/developers",
  "iconUrl": "https://classifier.dev/favicon.svg",
  "capabilities": {
    "streaming": false,
    "pushNotifications": false,
    "stateTransitionHistory": false
  },
  "securitySchemes": {},
  "security": [],
  "defaultInputModes": [
    "application/json",
    "text/plain"
  ],
  "defaultOutputModes": [
    "application/json",
    "text/plain"
  ],
  "skills": [
    {
      "id": "classify_texts",
      "name": "Classify texts into one label each",
      "description": "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 \u2014 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.",
      "tags": [
        "classification"
      ],
      "inputModes": [
        "application/json"
      ],
      "outputModes": [
        "application/json",
        "text/plain"
      ]
    },
    {
      "id": "classify_dimensions",
      "name": "Classify several dimensions per text",
      "description": "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 \u00d7 dimension decisions; every field counts toward the quota. Use per-dimension instructions to define ambiguous categories.",
      "tags": [
        "classification"
      ],
      "inputModes": [
        "application/json"
      ],
      "outputModes": [
        "application/json",
        "text/plain"
      ]
    },
    {
      "id": "classify_multi_label",
      "name": "Tag texts with every label that applies",
      "description": "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 \u2014 topics of an article, components touched by a ticket \u2014 where one answer is not enough. Set max_labels to cap how many come back per text. Labels scoring >= 0.7 are kept.",
      "tags": [
        "classification"
      ],
      "inputModes": [
        "application/json"
      ],
      "outputModes": [
        "application/json",
        "text/plain"
      ]
    },
    {
      "id": "count_labels",
      "name": "Count how many texts fall under each label",
      "description": "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 \u2014 what share of feedback is bugs vs praise, how many search results are relevant \u2014 without pulling a thousand individual answers into context. Use classify_texts when you need the answer per item.",
      "tags": [
        "classification"
      ],
      "inputModes": [
        "application/json"
      ],
      "outputModes": [
        "application/json",
        "text/plain"
      ]
    },
    {
      "id": "review_uncertain",
      "name": "Find the texts the classifier was unsure about",
      "description": "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.",
      "tags": [
        "classification"
      ],
      "inputModes": [
        "application/json"
      ],
      "outputModes": [
        "application/json",
        "text/plain"
      ]
    }
  ]
}