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JevK5

JevK5-2B reads a text and typed questions, then chooses one of the answers you listed for each question. CPU only. The same call returns the decisions plus usage and latency.

Service id: jevk5
Version: 0.2.0
Status: available

Canonical payloads: https://mcp.glc-rag.hu/guide/jevk5/payload · markdown · docs://jevk5/payload

Authentication

MCP endpoint: https://mcp.glc-rag.hu/mcp (streamable HTTP)

Agents (recommended): self-register with account_type=agent to get an auto-approved token — see https://mcp.glc-rag.hu/guide/agent.

Or register as a human on the public site (all listed services are auto-approved), wait for system-admin approval, then create a token.

Authorization: Bearer mcp_...

Cursor mcp.json example:

{
  "mcpServers": {
    "jevk5": {
      "url": "https://mcp.glc-rag.hu/mcp",
      "headers": {
        "Authorization": "Bearer mcp_YOUR_TOKEN"
      }
    }
  }
}

Tools

jevk5_status

Whether JevK5-2B is loaded, plus the CPU thread budget. Free — 0 credits.

Input schema:

{
  "type": "object",
  "properties": {},
  "additionalProperties": false
}

Examples:

{}

jevk5_decide — Decide

Pick one allowed answer per question with JevK5-2B. Pass text and questions (each with id, question, and options). Returns answers plus usage and latency. 1 credit per 10 questions.

Input schema:

{
  "type": "object",
  "additionalProperties": false,
  "required": [
    "text",
    "questions"
  ],
  "properties": {
    "text": {
      "type": "string",
      "minLength": 1,
      "maxLength": 60000,
      "description": "Text the questions are about"
    },
    "questions": {
      "type": "array",
      "minItems": 1,
      "maxItems": 32,
      "description": "Questions and the answers the model may pick",
      "items": {
        "type": "object",
        "additionalProperties": false,
        "required": [
          "id",
          "question",
          "options"
        ],
        "properties": {
          "id": {
            "type": "string",
            "minLength": 1,
            "maxLength": 64
          },
          "question": {
            "type": "string",
            "minLength": 1,
            "maxLength": 500
          },
          "options": {
            "type": "array",
            "minItems": 2,
            "maxItems": 32,
            "uniqueItems": true,
            "items": {
              "type": "string",
              "minLength": 1,
              "maxLength": 200
            }
          }
        }
      }
    }
  }
}

Examples:

{
  "text": "J\u00f3 napot! A m\u00falt heti rendel\u00e9sem (R-1842) m\u00e9g mindig nem \u00e9rkezett meg, holnapra viszont m\u00e1r kellene. K\u00e9rem, n\u00e9zz\u00e9k meg, mi t\u00f6rt\u00e9nt, \u00e9s ha lehet, k\u00fcldj\u00e9k ki m\u00e9g ma.",
  "questions": [
    {
      "id": "tema",
      "question": "Mir\u0151l sz\u00f3l a lev\u00e9l?",
      "options": [
        "sz\u00e1ll\u00edt\u00e1s",
        "sz\u00e1mla",
        "\u00faj aj\u00e1nlat",
        "egy\u00e9b"
      ]
    },
    {
      "id": "surgosseg",
      "question": "Mennyire s\u00fcrg\u0151s?",
      "options": [
        "r\u00e1\u00e9r",
        "hamarosan",
        "ma kell"
      ]
    }
  ]
}

Usage notes

Call jevk5_decide with text and questions. Each question has id, question, and options (the allowed answers). The response answers list has one row per question: answer is one of that question’s options, scores is the probability of each option, confidence is the model’s confidence in the pick.

Also returned: usage.input_tokens / usage.output_tokens (always 0), latency_ms (model time), client_wall_ms (full tool wall time).

Billing: 1 credit per 10 questions, rounded up (1–10 costs 1, 11–20 costs 2). jevk5_status is free. A failed call is not billed.

Backend: alibiserikbay/JevK5 on CPU. Context up to 16_384 tokens. At most 32 questions and 32 options per question (options beyond 16 use knockout passes). Prefer clear option labels (e.g. igaz/hamis, not IGAZ/HAMIS).

Errors / limits

Missing text, duplicate ids or options, or a model failure → {error, is_error}. Those calls cost 0 credits.

Agent discovery