assess_admissions_fit학생의 시험 점수(SAT/ACT)와 목표 대학 목록을 받아, 확인 가능한 College Scorecard 실데이터 기반으로 학교별 REACH/MATCH/SAFETY 적합도를 분류합니다. 합격 확률 예측이 아닌 밴드 분류이며, 데이터가 있는 경우 점수 밴드·합격률 근거를 함께 반환합니다.
| Name | Required | Description | Default |
|---|---|---|---|
| act | No | ACT 총점 | |
| sat | No | SAT 총점 | |
| target_universities | Yes | 목표 대학 이름 목록 (영문/한글/약칭 허용, 예: MIT, Harvard) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. Description adds value by revealing data source (College Scorecard) and that it returns score bands/admission rate evidence when available. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two well-structured sentences: first sentence states core action, second clarifies scope and return. No wasted words, front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a classification tool with 3 parameters and no output schema, the description explains the output (reach/match/safety bands with possible evidence) and data source. Missing error conditions or edge cases, but adequate overall.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with clear descriptions for all 3 parameters. The description summarizes the inputs but does not add detailed syntax or format beyond the schema. Baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description uses specific verb '분류합니다' and resource '입시 적합도', clearly stating it classifies reach/match/safety based on College Scorecard data. Sibling tools (capture_lead, get_booking_options, etc.) are distinct, so no confusion.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description implies use when test scores and target university list are available, and explicitly clarifies it is not a probability prediction ('합격 확률 예측이 아닌'). No explicit when-not or alternatives, but usage context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: assess fit, capture lead, get booking options, list universities, and search mentors. Even though assess_admissions_fit and list_supported_universities both relate to universities, the former is an actionable assessment while the latter is a reference list, and the descriptions remove any ambiguity.
All tool names follow a consistent verb_noun pattern (assess, capture, get, list, search) with lowercase underscores. There is no mixing of styles or vague verbs, making the naming predictable and easy to navigate.
With 5 tools, the server is well-scoped for a college admissions storefront. Each tool serves a clear function in the user journey—discovering universities, assessing fit, finding mentors, booking sessions, and capturing leads—without unnecessary bloat or missing essentials.
The tool set covers the core lifecycle of a prospective student: exploring supported universities, assessing admission fit, searching for mentors, obtaining booking options, and leaving contact info. There are no obvious dead ends; the tools work together smoothly to support the main workflows.