prefrosh.ai
College admissions: admit rates by round, your odds, compare colleges, and a mock admissions game.
Should I use this
Quality & Safety
Findings (1)
- LOWTool 'game_vote' description lacks action verbin game_vote
Based on automated analysis of tool definitions and protocol compliance.
Context Cost
This is the approximate number of tokens consumed each time the server's tools are loaded into a model's context. Higher counts reduce the attention available for other tasks.
Install
One-Click Install
Add this to your `claude_desktop_config.json` file:
{
"mcpServers": {
"prefrosh": {
"url": "https://prefrosh.ai/mcp"
}
}
}Remote endpoints
https://prefrosh.ai/mcpstreamable-httpWhat it can do
Tool inventory
Tools (8)
🟢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.
Input Schema
{
"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.
Input Schema
{
"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).
Input Schema
{
"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.
Input Schema
{
"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.
Input Schema
{
"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.
Input Schema
{
"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.
Input Schema
{
"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).
Input Schema
{
"type": "object",
"properties": {
"school": {
"type": "string"
},
"usage": {
"type": "object"
},
"seconds": {
"type": "number"
}
}
}Community
Evidence