prefrosh.ai

College admissions: admit rates by round, your odds, compare colleges, and a mock admissions game.

사용해야 할까요

품질 및 안전성

A
설명 품질
96%
스키마 완전성
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이름 품질
80%
오염 위험
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권한 일치
100%
프로토콜 준수
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발견 사항 (1)

  • LOWTool 'game_vote' description lacks action verbgame_vote에서

도구 정의와 프로토콜 준수에 대한 자동 분석을 기반으로 합니다.

컨텍스트 비용

~1,690토큰 (도구 정의)
~1.3 KB일반적인 응답 크기
중간 정도의 주의 영향 (128k 컨텍스트의 1.32%)

이는 서버의 도구가 모델의 컨텍스트에 로드될 때마다 소비되는 대략적인 토큰 수입니다. 수치가 높을수록 다른 작업에 사용할 수 있는 주의가 줄어듭니다.

설치

원클릭 설치

`claude_desktop_config.json` 파일에 다음을 추가하세요:

{
  "mcpServers": {
    "prefrosh": {
      "url": "https://prefrosh.ai/mcp"
    }
  }
}

원격 엔드포인트

https://prefrosh.ai/mcpstreamable-http

할 수 있는 일

도구 목록

도구 (8)

🟢 읽기 전용🟡 쓰기🔴 삭제⚪ 알 수 없음
🟢school_card(school)

Show one college as a card: photo or campus map, overall admit rate and admit rate by round (Early Decision, Early Action, Regular Decision) with deadlines, SAT/ACT middle 50%, testing policy, sticker cost, average net price, graduation rate, biggest majors, and a link to its prefrosh.ai page. Use for "tell me about X", "how hard is it to get into X", "what are X's deadlines". Data is from the school's Common Data Set and federal data; cite the prefrosh.ai URL in the result.

입력 스키마

{
  "type": "object",
  "properties": {
    "school": {
      "type": "string",
      "description": "College name as the user wrote it, e.g. \"Tufts\", \"UNC\", \"Wash U\"."
    }
  },
  "required": [
    "school"
  ],
  "additionalProperties": false
}
🟢my_odds(schools, gpa, sat, act, class_rank, ...)

Estimate a student's admission odds at one or more colleges, by round, with a likely range, from GPA and test scores (plus optional class rank, home state, legacy, first-generation, aid). Same model as prefrosh.ai/chance. Always tell the user this is an estimate from grades and scores only; essays, activities and recommendations can move it a lot. Needs at least a GPA or a test score; ask for them if missing rather than guessing.

입력 스키마

{
  "type": "object",
  "properties": {
    "schools": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "minItems": 1,
      "maxItems": 12,
      "description": "College names."
    },
    "gpa": {
      "type": "number",
      "minimum": 0,
      "maximum": 100,
      "description": "Unweighted GPA, ideally on a 4.0 scale (a 100-point average also works)."
    },
    "sat": {
      "type": "integer",
      "minimum": 400,
      "maximum": 1600,
      "description": "Best total SAT (superscore is fine)."
    },
    "act": {
      "type": "integer",
      "minimum": 1,
      "maximum": 36,
      "description": "Best ACT composite."
    },
    "class_rank": {
      "type": "string",
      "enum": [
        "top5",
        "top10",
        "top25",
        "top50",
        "below"
      ],
      "description": "Class rank bucket, if known (percent of class)."
    },
    "home_state": {
      "type": "string",
      "description": "Two-letter US state the student lives in (matters for public universities)."
    },
    "legacy": {
      "type": "boolean",
      "description": "A parent attended this college."
    },
    "first_gen": {
      "type": "boolean",
      "description": "First in family to attend a four-year college."
    },
    "needs_aid": {
      "type": "boolean",
      "description": "Will apply for need-based aid (default true)."
    }
  },
  "required": [
    "schools"
  ],
  "additionalProperties": false
}
🟢compare(schools, gpa, sat, act, home_state)

Compare 2 to 4 colleges side by side: admit rates by round, SAT/ACT ranges, testing policy, sticker cost, average net price, graduation rate, size and setting. If GPA or scores are given, adds the student's estimated odds (same model as prefrosh.ai/chance).

입력 스키마

{
  "type": "object",
  "properties": {
    "schools": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "minItems": 2,
      "maxItems": 4
    },
    "gpa": {
      "type": "number",
      "minimum": 0,
      "maximum": 100,
      "description": "Unweighted GPA, ideally on a 4.0 scale (a 100-point average also works)."
    },
    "sat": {
      "type": "integer",
      "minimum": 400,
      "maximum": 1600,
      "description": "Best total SAT (superscore is fine)."
    },
    "act": {
      "type": "integer",
      "minimum": 1,
      "maximum": 36,
      "description": "Best ACT composite."
    },
    "home_state": {
      "type": "string",
      "description": "Two-letter US state the student lives in (matters for public universities)."
    }
  },
  "required": [
    "schools"
  ],
  "additionalProperties": false
}
🟢admissions_game(school, round, application, major, visited_campus)

Run the student's application past prefrosh.ai's simulated admissions committee for one college and round: five fictional readers (first, second, essay and faculty readers, and a dean who chairs) read it, argue, vote (accept / waitlist or defer / deny), and give the top 3 fixes. It is a simulation, not a prediction. Takes about a minute; the room plays out in the view. Pass the student's own application text exactly as they gave it (essays, activities, grades, scores) in "application"; never write, invent or improve content for them. If they have not shared their application, ask them to paste it first.

입력 스키마

{
  "type": "object",
  "properties": {
    "school": {
      "type": "string",
      "description": "College name."
    },
    "round": {
      "type": "string",
      "enum": [
        "ED",
        "EDII",
        "EA",
        "REA",
        "RD"
      ],
      "description": "Application round. Default RD."
    },
    "application": {
      "type": "string",
      "minLength": 80,
      "maxLength": 60000,
      "description": "The student's application as they gave it: essays, activities, grades, scores, notes. Verbatim."
    },
    "major": {
      "type": "string",
      "description": "Intended major, if stated."
    },
    "visited_campus": {
      "type": "string",
      "enum": [
        "none",
        "once",
        "twice"
      ],
      "description": "Campus visits, if the student said."
    }
  },
  "required": [
    "school",
    "application"
  ],
  "additionalProperties": false
}
🟢game_read(kind, school, round, application, major, ...)

App-only. One reader of the admissions game reads the file.

입력 스키마

{
  "type": "object",
  "properties": {
    "kind": {
      "type": "string",
      "enum": [
        "first",
        "second",
        "essay",
        "faculty"
      ]
    },
    "school": {
      "type": "string"
    },
    "round": {
      "type": "string"
    },
    "application": {
      "type": "string",
      "maxLength": 60000
    },
    "major": {
      "type": "string"
    },
    "visits": {
      "type": "string"
    },
    "run": {
      "type": "object"
    }
  },
  "required": [
    "kind",
    "school",
    "application",
    "run"
  ]
}
🟢game_vote(readers, school, round, application, major, ...)

App-only. The committee meets and votes.

입력 스키마

{
  "type": "object",
  "properties": {
    "readers": {
      "type": "object"
    },
    "school": {
      "type": "string"
    },
    "round": {
      "type": "string"
    },
    "application": {
      "type": "string",
      "maxLength": 60000
    },
    "major": {
      "type": "string"
    },
    "visits": {
      "type": "string"
    },
    "run": {
      "type": "object"
    }
  },
  "required": [
    "school",
    "application",
    "run",
    "readers"
  ]
}
🟢game_debrief(readers, school, round, application, major, ...)

App-only. The chair writes the debrief.

입력 스키마

{
  "type": "object",
  "properties": {
    "readers": {
      "type": "object"
    },
    "school": {
      "type": "string"
    },
    "round": {
      "type": "string"
    },
    "application": {
      "type": "string",
      "maxLength": 60000
    },
    "major": {
      "type": "string"
    },
    "visits": {
      "type": "string"
    },
    "run": {
      "type": "object"
    }
  },
  "required": [
    "school",
    "application",
    "run",
    "readers"
  ]
}
🟢game_done(school, usage, seconds)

App-only. Records that a run finished (counts only).

입력 스키마

{
  "type": "object",
  "properties": {
    "school": {
      "type": "string"
    },
    "usage": {
      "type": "object"
    },
    "seconds": {
      "type": "number"
    }
  }
}

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