classifier.dev
REMOTE · CLASSIFIER.DEV · SCANNED SEP 28
Sort up to 1,000 texts into your own labels with a calibrated confidence per answer. No API key.
Available components
How this component scores in each security and reliability category. Every signal is checked automatically against the live server, and we only credit what we can confirm. How we score → Why this is hard to score →
Endpoint Security63
- The endpoint's TLS certificate is valid, in date, and uses a strong key. View diagnostics → Pass
- No authorisation is required to call this server. Every tool declares its destructiveHint and none is destructive, so open access doesn't expose one. See how to fix → View diagnostics → Partial
- HTTPS enforcement could not be verified: the plaintext port answered with HTTP 405, which proves neither a plaintext path nor enforcement. View diagnostics → Unverified
- HSTS check failed: the Strict-Transport-Security header is absent. See how to fix → View diagnostics → Fail
- DNSSEC check failed: this domain isn't protected by DNSSEC. See how to fix → View diagnostics → Fail
Transport & Reachability100
- Verified streamable-http transport via a live MCP handshake. View diagnostics → Pass
Schema Quality & AI Usability60
- AI-judged instruction clarity (excellent).Pass
- Context-footprint check failed: tool/resource definitions use about 1863 tokens (~372/item across 5 items; 5 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management30
- Stability observed for 9 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage98
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 94% of tool parameters carry a description.Partial
- Structured output schemas are declared (80% of tools); any adoption earns full credit.Pass
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 5 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 6 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass
How do I install the classifier.dev MCP server?
classifier.dev is a hosted endpoint at https://classifier.dev/mcp, so there is nothing to install locally. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
remote · classifier.dev
claude mcp add --transport http dev-classifier-classifier 'https://classifier.dev/mcp'
{
"mcpServers": {
"dev-classifier-classifier": {
"url": "https://classifier.dev/mcp"
}
}
} {
"servers": {
"dev-classifier-classifier": {
"type": "http",
"url": "https://classifier.dev/mcp"
}
}
} [mcp_servers.dev-classifier-classifier] url = "https://classifier.dev/mcp"
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"dev-classifier-classifier": {
"type": "remote",
"url": "https://classifier.dev/mcp",
"enabled": true
}
}
} openclaw mcp add dev-classifier-classifier --url 'https://classifier.dev/mcp' --transport streamable-http
mcp_servers:
dev-classifier-classifier:
url: "https://classifier.dev/mcp" {
"McpServers": {
"dev-classifier-classifier": {
"Transport": "http",
"Url": "https://classifier.dev/mcp"
}
}
} assistant mcp add dev-classifier-classifier -t streamable-http -u 'https://classifier.dev/mcp'
{
"mcpServers": {
"dev-classifier-classifier": {
"type": "http",
"url": "https://classifier.dev/mcp"
}
}
} The mcpServers block is a cross-client convention. Remote transports vary, so check your client's docs.
Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 28 Sept 26 0
- We updated how we score, so this day's move reflects our rubric, not a change to the server See what changed → functional
- 27 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 23 to 27. That category is still filling its 30-day observation window: 7 days of observed history at the previous scan, 8 at this one. The score rises as the window fills, whether or not the server changes.
- 25 Sept 26 0
- We updated how we score, so this day's move reflects our rubric, not a change to the server See what changed → functional
- 24 Sept 26 +1
- “classify_texts” reworded the description of “model” cosmetic
1 cosmetic change on this day. Switch on “Show cosmetic changes” to see it.
- 23 Sept 26 0
- “classify_texts” reworded the description of “model” cosmetic
1 cosmetic change on this day. Switch on “Show cosmetic changes” to see it.
- 22 Sept 26 +1
- “classify_texts” reworded the description of “model” cosmetic
1 cosmetic change on this day. Switch on “Show cosmetic changes” to see it.
- 21 Sept 26 −1
- Tool “classify_texts” rewrote its description, which is the text the model reads security
- Schema quality: 257 → 296 ▼ functional
- Tool coverage: 100% → 92% ▼ functional
- “classify_dimensions” added an optional parameter “model” cosmetic
- “classify_dimensions” added an optional parameter “processing” cosmetic
- “classify_multi_label” added an optional parameter “model” cosmetic
- “classify_multi_label” added an optional parameter “processing” cosmetic
- “classify_texts” added an optional parameter “model” cosmetic
- “classify_texts” added an optional parameter “processing” cosmetic
- “classify_dimensions” reworded the description of “tier” cosmetic
- “classify_texts” reworded the description of “tier” cosmetic
- 20 Sept 26 +1
- The server rewrote its instructions, which are the text every model session reads security
- Tool coverage: 100% → 80% ▼ functional
- Schema quality: 1072 → 1287 ▼ functional
- Stability: unverified → 0.03 ▲ functional
- New tool “classify_dimensions” functional
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 28 Sept 2026 · Probed https://classifier.dev/mcp
TLS valid
Negotiated TLS 1.3 with TLS_AES_128_GCM_SHA256 .
| Subject | Issuer | Valid from | Valid until | Key | Signature | Serial |
|---|---|---|---|---|---|---|
| CN=classifier.dev | CN=WE1,O=Google Trust Services,C=US | 13 Aug 2026 | 11 Nov 2026 | ECDSA 256 | ECDSA-SHA256 | 53f2a38558bc9b850eb3dd68ab54e54b |
| SANs: classifier.dev, www.classifier.dev, *.www.classifier.dev | ||||||
| CN=WE1,O=Google Trust Services,C=US (CA) | CN=GTS Root R4,O=Google Trust Services LLC,C=US | 13 Dec 2023 | 20 Feb 2029 | ECDSA 256 | ECDSA-SHA384 | 7ff31977972c224a76155d13b6d685e3 |
| CN=GTS Root R4,O=Google Trust Services LLC,C=US (CA) | CN=GlobalSign Root CA,OU=Root CA,O=GlobalSign nv-sa,C=BE | 15 Nov 2023 | 28 Jan 2028 | ECDSA 384 | SHA256-RSA | 7fe530bf331343bedd821610493d8a1b |
Background: What to check on a remote MCP endpoint →
DNSSEC insecure
Validation of classifier.dev. — Not signed
| Zone | DS | Keys | Algorithms | Outcome |
|---|---|---|---|---|
| . | trust_anchor | 20326, 38696 | 8, 8 | Verified |
| dev. | present | 60074 | 8 | Verified |
| classifier.dev. | absent | Unsigned (proven) parent-signed NSEC/NSEC3 proves an unsigned delegation |
Authentication No authorisation required
The endpoint answered without asking for a token. Anyone who knows the URL can reach it.
| Result | No authorisation required |
|---|---|
| HTTP status | 200 |
Background: How OAuth 2.1 works in the 2026 MCP spec →
Transports 2 probes
| Transport | URL | Outcome | Status | Location |
|---|---|---|---|---|
| streamable-http | https://classifier.dev/mcp | Verified | 200 | |
| http (plaintext) | http://classifier.dev/mcp | Inconclusive | 405 |
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
classify_dimensions Classify several dimensions per text ~329
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.
| Name | Type | Req | Description |
|---|---|---|---|
| dimensions | object | yes | Named dimensions. Each is a label array or {labels, instructions}. At most 1,000 item × dimension decisions; definitions at most 16,000 characters combined. |
| include | array | – | With url: return article.markdown and/or article.html at no extra scrape cost. Omit for compact output. |
| instructions | string | – | Optional extra criteria, e.g. "judge only the service, ignore the food". |
| items | array | – | 1 to 1,000 texts to classify. Results come back in the same order. |
| model | string | – | – |
| processing | string | – | Optional. Implies Laya if model is omitted; has no effect with explicit Jev. Omit for automatic fast/bulk selection based on item × dimension decisions. |
| tier | string | – | fast (default) or smart, which re-asks answers under 0.7 confidence of a reasoning model (slower, single-label only). Independent of the Laya processing lane. |
| url | string | – | Scrape one public URL instead of inputs/items. Requires funded workspace access. Context.dev costs $0.0022 per billed attempt plus classification; long articles need Fast. Errors disclose retained ch… |
No output schema declared.
No examples provided.
classify_multi_label Tag texts with every label that applies ~330
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.
| Name | Type | Req | Description |
|---|---|---|---|
| include | array | – | With url: return article.markdown and/or article.html at no extra scrape cost. Omit for compact output. |
| inputs | array | – | 1 to 1,000 texts to classify. Results come back in the same order. |
| instructions | string | – | Optional extra criteria, e.g. "judge only the service, ignore the food". |
| labels | array | yes | 2 to 100 category names. Descriptive names classify better: "urgent bug" beats "p0". Add a label like "none of these" when none-of-the-above is a real outcome. |
| max_labels | integer | – | At most this many labels per text, most likely first. |
| model | string | – | – |
| processing | string | – | Optional. Implies Laya if model is omitted; has no effect with explicit Jev. Omit to select fast for up to four labels on one text, or bulk for larger work automatically. |
| url | string | – | Scrape one public URL instead of inputs/items. Requires funded workspace access. Context.dev costs $0.0022 per billed attempt plus classification; long articles need Fast. Errors disclose retained ch… |
| Name | Type | Req | Description |
|---|---|---|---|
| results | array | yes | – |
| usage | object | – | – |
No examples provided.
classify_texts Classify texts into one label each ~617
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.
| Name | Type | Req | Description |
|---|---|---|---|
| include | array | – | With url: return article.markdown and/or article.html at no extra scrape cost. Omit for compact output. |
| inputs | array | – | 1 to 1,000 texts to classify. Results come back in the same order. |
| instructions | string | – | Optional extra criteria, e.g. "judge only the service, ignore the food". |
| labels | array | yes | 2 to 100 category names. Descriptive names classify better: "urgent bug" beats "p0". Add a label like "none of these" when none-of-the-above is a real outcome. |
| model | string | – | Jev is default. Default/explicit jev inputs over 32,000 characters use paid Fast-only long context: up to 250,000 original cl100k_base context tokens total, 20 documents, 32 decisions and a 1 MB body… |
| processing | string | – | Optional. Implies Laya if model is omitted; has no effect with explicit Jev. With Laya, omit to select fast for one decision or bulk for batches automatically. Explicit fast accepts one decision. Sha… |
| tier | string | – | fast (default) or smart, which re-asks answers under 0.7 confidence of a reasoning model (slower, single-label only). Independent of the Laya processing lane. |
| url | string | – | Scrape one public URL instead of inputs/items. Requires funded workspace access. Context.dev costs $0.0022 per billed attempt plus classification; long articles need Fast. Errors disclose retained ch… |
| Name | Type | Req | Description |
|---|---|---|---|
| model | string | – | – |
| results | array | yes | One per input, in input order. |
| tier | string | – | – |
| usage | object | – | – |
No examples provided.
count_labels Count how many texts fall under each label ~201
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.
| Name | Type | Req | Description |
|---|---|---|---|
| inputs | array | yes | 1 to 1,000 texts to classify. Results come back in the same order. |
| instructions | string | – | Optional extra criteria, e.g. "judge only the service, ignore the food". |
| labels | array | yes | 2 to 100 category names. Descriptive names classify better: "urgent bug" beats "p0". Add a label like "none of these" when none-of-the-above is a real outcome. |
| unsure_below | number | – | Answers with confidence under this count as unsure. |
| Name | Type | Req | Description |
|---|---|---|---|
| counts | object | yes | Label -> how many texts, every label present. |
| total | integer | yes | – |
| unsure | integer | yes | How many answers fell under unsure_below. |
| unsure_below | number | – | – |
No examples provided.
review_uncertain Find the texts the classifier was unsure about ~198
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.
| Name | Type | Req | Description |
|---|---|---|---|
| below | number | – | Return items with confidence under this. |
| inputs | array | yes | 1 to 1,000 texts to classify. Results come back in the same order. |
| instructions | string | – | Optional extra criteria, e.g. "judge only the service, ignore the food". |
| labels | array | yes | 2 to 100 category names. Descriptive names classify better: "urgent bug" beats "p0". Add a label like "none of these" when none-of-the-above is a real outcome. |
| Name | Type | Req | Description |
|---|---|---|---|
| below | number | – | – |
| total | integer | yes | – |
| uncertain | array | yes | – |
No examples provided.
What is the classifier.dev MCP server?
classifier.dev is an MCP server listed in the public MCP registry as dev.classifier/classifier. Sort up to 1,000 texts into your own labels with a calibrated confidence per answer. No API key. This page covers its hosted endpoint (https://classifier.dev/mcp).
Is the classifier.dev MCP server safe to use?
classifier.dev scores 68 out of 100 on VerifyMCP. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the classifier.dev MCP server expose?
classifier.dev exposes 5 tools: classify_texts, classify_dimensions, classify_multi_label, count_labels, review_uncertain. Their descriptions and schemas cost roughly 1,675 tokens of context every time the server is loaded.
Does the classifier.dev MCP server require authentication?
No. We connected to classifier.dev without credentials and it answered, so anything it exposes is reachable by anyone who knows the address.
Is the classifier.dev MCP server still maintained?
classifier.dev is still listed as active in the MCP registry. We last reached this channel on 28 September 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.