CELPass: CELPIP practice and CRS calculator

CELPIP practice questions with answers, sample answers, and Canada's November 2026 CRS calculator.

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Vollständigkeit des Schemas
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Basierend auf einer automatisierten Analyse der Tool-Definitionen und der Einhaltung des Protokolls.

Kontextkosten

~1,204Tokens (Tool-Definitionen)
~1.1 KBTypische Antwortgröße
Mittlere Auswirkung auf die Aufmerksamkeit (0.94% von 128k Kontext)

Dies ist die ungefähre Anzahl der Tokens, die jedes Mal verbraucht werden, wenn die Tools des Servers in den Kontext eines Modells geladen werden. Höhere Werte verringern die Aufmerksamkeit, die für andere Aufgaben verfügbar ist.

Installieren

Installation mit einem Klick

Fügen Sie dies Ihrer Datei `claude_desktop_config.json` hinzu:

{
  "mcpServers": {
    "celpip": {
      "url": "https://celpass.com/mcp"
    }
  }
}

Remote-Endpunkte

https://celpass.com/mcpstreamable-http

Was es kann

Tool-Inventar

Tools (5)

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🟢calculate_crs_november_2026(current_crs_score, spouse_counted, canadian_work_years, high_wage_jobs, job_offer_noc_code)

Estimate the CRS points an Express Entry candidate gains on November 3, 2026, when Canadian work experience becomes the Labour Market Integration factor: unchanged work-length points plus 70/40/20 extra points after 12+ months in a 2×/1.5×/1.3× high-wage occupation, or 100/70/50 points for a job offer in one (no LMIA needed), whichever is higher, never both. Max 150 (140 with a spouse). Give NOC codes; the tool looks up each occupation's level on IRCC's list. Only this factor changes.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "current_crs_score": {
      "type": "integer",
      "minimum": 0,
      "maximum": 1200,
      "description": "CRS score in the profile today (optional)"
    },
    "spouse_counted": {
      "type": "boolean",
      "description": "True if a spouse or common-law partner is coming to Canada and isn't a citizen or PR"
    },
    "canadian_work_years": {
      "type": "integer",
      "minimum": 0,
      "maximum": 5,
      "description": "Years of skilled (TEER 0–3) work in Canada in the last 10 years, as counted today; 5 means 5 or more"
    },
    "high_wage_jobs": {
      "type": "array",
      "description": "Canadian jobs (part of that work experience) and the months spent in each. Omit if none.",
      "items": {
        "type": "object",
        "properties": {
          "noc_code": {
            "type": "string",
            "pattern": "^\\d{5}$",
            "description": "5-digit NOC 2021 code, e.g. 21231"
          },
          "months": {
            "type": "number",
            "minimum": 0
          }
        },
        "required": [
          "noc_code",
          "months"
        ]
      }
    },
    "job_offer_noc_code": {
      "type": "string",
      "pattern": "^\\d{5}$",
      "description": "NOC code of a qualifying full-time job offer, if any"
    }
  },
  "required": [
    "spouse_counted",
    "canadian_work_years"
  ]
}
🟢find_high_wage_occupation(query)

Search IRCC's high-wage occupation list (in effect November 3, 2026; 186 NOC 2021 codes) by job title words or NOC code. Returns each match's level (2×, 1.5× or 1.3× the average median wage) and the CRS points it earns. An occupation that isn't listed earns no high-wage or job offer points.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "minLength": 1,
      "description": "Job title words or a NOC code, e.g. 'software engineer' or '72201'"
    }
  },
  "required": [
    "query"
  ]
}
🟢get_celpip_practice_question(skill, part)

Get one random CELPIP practice item from CELPass in the real test format. Reading (parts 1–4): a passage or diagram with multiple-choice questions and fill-in blanks shown as ____(n). Listening (parts 1–6): the questions plus a listen_url where the learner plays the recording (no transcript). Speaking (parts 1–8): the task, any picture, and the preparation and speaking times. Writing (tasks 1–2): the email or survey task with its time limit and word count. No answers are included: mark Reading and Listening with check_celpip_answers, and compare Speaking and Writing with get_celpip_sample_answers. Each day has a new set of items (3 per part), the same for everyone; calls return a random one from today's set.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "skill": {
      "type": "string",
      "enum": [
        "reading",
        "listening",
        "speaking",
        "writing"
      ],
      "description": "Default reading"
    },
    "part": {
      "type": "integer",
      "minimum": 1,
      "maximum": 8,
      "description": "Part number (Reading 1–4, Listening 1–6, Speaking 1–8, Writing 1–2); omit for any part"
    }
  }
}
🟢check_celpip_answers(question_id, answers)

Mark a learner's answers to a Reading or Listening item from get_celpip_practice_question: returns the score, the correct letter for each question and an explanation of why each option is right or wrong.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "question_id": {
      "type": "string",
      "description": "question_id returned with the question"
    },
    "answers": {
      "type": "object",
      "description": "Question number → chosen letter, e.g. {\"1\": \"C\", \"2\": \"A\"}",
      "additionalProperties": {
        "type": "string"
      }
    }
  },
  "required": [
    "question_id",
    "answers"
  ]
}
🟢get_celpip_sample_answers(question_id)

Sample answers at several CELPIP levels (4–5, 7–8, 9–10, 11–12) for a Speaking or Writing task from get_celpip_practice_question, with how CELPIP rates it. Use them to give the learner feedback on their own answer.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "question_id": {
      "type": "string",
      "description": "question_id returned with the Speaking or Writing task"
    }
  },
  "required": [
    "question_id"
  ]
}

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