# Classifier MCP server

Source: https://glimind.com/server/mcp-registry%2Fdev.classifier%2Fclassifier
Registry id: `mcp-registry/dev.classifier/classifier`
Status: live (checked 2026-10-26T23:13:49.427Z)

## Verdict

Classifier MCP server answered 100% of Glimind's liveness checks over 30 days, better than 42% of MCP servers, with 341 ms median handshake and 0 outages in 90 days. Its agent-readiness grade is A (90/100) across 5 tools, costing an agent about 3,674 tokens to load. Checked every few minutes; no tool is ever called.

## Measurements (30 days)

| Metric | Value | Better than |
|---|---|---|
| Uptime, 30 days | 100% | 42% of MCP servers |
| Median handshake latency | 341 ms | 59% of MCP servers |
| p95 handshake latency | 2636 ms | — |
| Checks in 30 days | 4,929 | — |
| Outages, 90 days | 0 | — |
| Median recovery | — | — |
| Agent-readiness score | 90/100 (A) | 69% of MCP servers |
| Tools | 5 | — |
| Tool-list size for an agent | ~3,674 tokens | 42% of MCP servers |
| Days observed | 29 | — |

## Install

```json
{
  "mcpServers": {
    "classifier-mcp-server": {
      "type": "http",
      "url": "https://classifier.dev/mcp"
    }
  }
}
```

Claude Code:

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

## Tools (5)

- `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
- `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 a
- `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 bucke
- `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
- `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 

Measured by Glimind (https://glimind.com): MCP handshake + tool list only, no tool is ever called. CC BY 4.0.