classifier.dev
An A2A client finds this agent by fetching the well-known path on the provider's own host. That is what makes an agent card different from every other agent artifact in this catalog: it is provider-published by construction — it cannot be derived, generated, or reconstructed on a provider's behalf.
https://classifier.dev/mcp
· transport JSONRPC
· protocol mcp
https://classifier.dev/
· transport HTTP+JSON
· protocol rest
Classify texts into one label each 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.
classificationClassify several dimensions per text 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.
classificationTag texts with every label that applies 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.
classificationCount how many texts fall under each label 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.
classificationFind the texts the classifier was unsure about 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.
classification2026-10-29 from https://classifier.dev/.well-known/agent-card.json,
HTTP 200. The body was saved verbatim and is the sole source for
everything on this page — no field is inferred.
View the captured card.
Providers change what they serve; if this card has moved or changed shape,
the provider profile carries the
current state as of the last build.
Work with this as data
Every agent card here is available over the APIs.io API and to AI agents over MCP. A2A Agent Cards is not yet its own endpoint on the v1 API. Reach it through catalog search and the tag graph, or the MCP server.