Jev Bulk Classifier

AI & Media Processing

Operated by Sourcelane✓ verified

A bulk text classification connector that converts lists of texts or existing text records into structured, typed columns by applying user-defined questions (categorical labels, ordered scores, and yes/no decisions). It performs multi-question classification in a single pass per text (supports up to 10 questions concurrently), returning constrained, machine-readable answers rather than free-form text. Technical outputs include selected categorical labels with confidence and per-option probability distributions, numeric ratings mapped to ordered levels with nearest-level labels and per-level probabilities, and boolean decisions with underlying probabilities; original text and source records are preserved for joinability. The connector is oriented toward large-scale automated labeling, scoring, and filtering workflows for text data.

Verified Sep 28, 12:10 AM

$0.002

per text

Up to 100 per call. Only pay for results returned; failed calls are refunded.

What people use it for

Support ticket triage: route messages to the correct team and flag urgent items
Review and comment analysis: sentiment, topic classification, and complaint detection across large volumes
Lead qualification: score scraped profiles or form submissions against custom criteria
Content moderation and spam filtering: flag off-topic, promotional, or abusive text with tunable probability thresholds
Cleaning up scraper output: label or filter rows from other scraping runs before downstream processing

Trust & reliability

7d uptime trend

Window	Uptime	Success rate	p50	p95
24h	—	—	—	—
7d	—	—	—	—
30d	—	—	—	—

Calling contract

Call it through Sourcelane's gateway or MCP server. We run the connector, apply your agent's spend guardrails, and bill only the results returned.

Call it via Sourcelane

curl -X POST https://cool-puffin-608.convex.site/v1/call \
  -H "Authorization: Bearer sl_live_your_agent_key" \
  -H "Content-Type: application/json" \
  -d '{"listing":"bulk-text-classifier","params":{"questions":[{"key":"department","type":"choice","instructions":"Which team should handle this message?","criteria":{"billing":"Payment or subscription issues","technical":"Bugs or integration problems","sales":"Pricing or account questions"}},{"key":"frustration","type":"score","instructions":"How frustrated the customer appears","criteria":["Calm, just stating facts","Frustrated but civil","Very angry, strong language"]},{"key":"is_urgent","type":"noul","instructions":"The message conveys urgency or time-sensitivity"}],"maxItems":5},"maxResults":10}'

Request params

Field	Type	Required	Description
`texts`	array	optional	The texts to evaluate — one per line. Leave empty if you are reading from an existing results instead.
`fields`	array	optional	When reading from a results, only send these fields to the model, e.g. 'text' and 'title'. Leave empty to send the whole record.
`questions`	array	required	The questions to ask about every text. Each question has a 'key' (the output column name), a 'type' ('choice' picks one option, 'score' rates against ordered levels, 'noul' answers yes/no), 'instructions', and 'criteria' (options for choice, ordered levels for score).
`maxItems`	integer	optional	Maximum number of texts to evaluate in this run. Set 0 for no limit. Capped at 100 per call.
`concurrency`	integer	optional	How many texts to evaluate in parallel. Higher is faster; lower it if you hit rate limits.

Each result contains

Field	Type	Description
`itemIndex`	integer	Item Index
`text`	string	Text
`source`	object	—
`has_text`	boolean	—
`has_text_probability`	number	—

Use Jev Bulk Classifier from Claude, ChatGPT or Cursor

Pick your tool and connect in under a minute. Then just ask — for example: “Transcribe this video and give me timestamps for every product mention: <url>”

Connect Claude

Web, desktop and mobile. Paste one URL.

1Copy your personal connector URL
2In Claude open Settings → Connectors → Add custom connector
3Paste the URL and click Add — done

Manual setup (config files, REST, Python) +

Claude Code

Adds the Sourcelane MCP server with your key as a header.

terminal

claude mcp add --transport http sourcelane https://cool-puffin-608.convex.site/mcp \
  --header "Authorization: Bearer sl_live_your_agent_key"

Claude Desktop (config file)

Alternative to the connector URL: add to claude_desktop_config.json, then restart Claude.

claude_desktop_config.json

{
  "mcpServers": {
    "sourcelane": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://cool-puffin-608.convex.site/mcp",
        "--header",
        "Authorization:${AUTH_HEADER}"
      ],
      "env": {
        "AUTH_HEADER": "Bearer sl_live_your_agent_key"
      }
    }
  }
}

Cursor & Windsurf

Add to ~/.cursor/mcp.json (or Windsurf's mcp_config.json).

mcp.json

{
  "mcpServers": {
    "sourcelane": {
      "url": "https://cool-puffin-608.convex.site/mcp",
      "headers": {
        "Authorization": "Bearer sl_live_your_agent_key"
      }
    }
  }
}

VS Code

Save as .vscode/mcp.json. VS Code prompts for your key once.

.vscode/mcp.json

{
  "servers": {
    "sourcelane": {
      "type": "http",
      "url": "https://cool-puffin-608.convex.site/mcp",
      "headers": {
        "Authorization": "Bearer ${input:sourcelane-key}"
      }
    }
  },
  "inputs": [
    {
      "type": "promptString",
      "id": "sourcelane-key",
      "description": "Sourcelane agent key",
      "password": true
    }
  ]
}

ChatGPT Custom GPT (Actions)

Create a GPT → Actions → Import from URL, then Authentication: API Key, Bearer.

OpenAPI schema URL

https://sourcelane-amber.vercel.app/openapi.json

REST

One POST. Pass maxResults to cap cost.

curl

curl -X POST https://cool-puffin-608.convex.site/v1/call \
  -H "Authorization: Bearer sl_live_your_agent_key" \
  -H "Content-Type: application/json" \
  -d '{"listing":"bulk-text-classifier","params":{"questions":[{"key":"department","type":"choice","instructions":"Which team should handle this message?","criteria":{"billing":"Payment or subscription issues","technical":"Bugs or integration problems","sales":"Pricing or account questions"}},{"key":"frustration","type":"score","instructions":"How frustrated the customer appears","criteria":["Calm, just stating facts","Frustrated but civil","Very angry, strong language"]},{"key":"is_urgent","type":"noul","instructions":"The message conveys urgency or time-sensitivity"}],"maxItems":5},"maxResults":10}'

Python, LangChain, CrewAI, OpenAI Agents SDK…

Wrap the REST call as a tool, or point an MCP client at the endpoint.

python

import requests

res = requests.post(
    "https://cool-puffin-608.convex.site/v1/call",
    headers={"Authorization": "Bearer sl_live_your_agent_key"},
    json={
        "listing": "bulk-text-classifier",
        "params": {"questions":[{"key":"department","type":"choice","instructions":"Which team should handle this message?","criteria":{"billing":"Payment or subscription issues","technical":"Bugs or integration problems","sales":"Pricing or account questions"}},{"key":"frustration","type":"score","instructions":"How frustrated the customer appears","criteria":["Calm, just stating facts","Frustrated but civil","Very angry, strong language"]},{"key":"is_urgent","type":"noul","instructions":"The message conveys urgency or time-sensitivity"}],"maxItems":5},
        "maxResults": 10,
    },
    timeout=300,
)
body = res.json()
print(body["receipt"]["chargedMicros"], "micro-USD for", body["receipt"]["results"], "results")
print(body["data"])

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