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Calculate match score

calculate_match_score

Given a user's GPA and test score, calculate match category (Safety/Target/Reach/Far Reach), admission probability, and percentiles for one school or a ranked list. Public — no authentication required.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
gpaNoCumulative GPA on a 4.0 scale (omit for ATAR-based Australian schools)
atarNoAustralian Tertiary Admission Rank (0-99.95). Used instead of test_score/gpa for Australian schools, which select on a published ATAR cutoff.
limitNoNumber of top schools to return when no school_slug given
programYesProgram type
test_scoreNoTest score: LSAT 120-180, MCAT 472-528, DAT 1-30, OAT 200-400, GMAT/GRE for MBA. Omit for pharmacy (PCAT retired 2024 — GPA-only) and for ATAR-based Australian schools.
school_slugNoIf provided, calculate for this school only. Otherwise returns a ranked list.
work_experience_yearsNoYears of full-time work experience (MBA programs only)

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the transparency burden. It discloses the authentication requirement (none) and specifies behavior (returns categories, probability, percentiles; operates for one school or ranked list). It does not state side effects (implicitly none), but the tool's read-only nature is evident from its purpose.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is one sentence, front-loaded with the action and outcome, with no superfluous words. It efficiently covers purpose, scope, and auth in under 30 words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers purpose, scope, authentication, and outputs, which is sufficient given the schema's rich parameter coverage. A minor gap is that it doesn't explicitly mention how parameters combine (e.g., ATAR vs GPA), but the schema handles this, so the description is adequately complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description mentions "GPA and test score" but does not add new meaning beyond the schema's detailed parameter descriptions (e.g., ATAR vs GPA, program-specific test ranges). It does not clarify ambiguities or add syntax/format guidance.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: "calculate match category (Safety/Target/Reach/Far Reach), admission probability, and percentiles" for "one school or a ranked list." It specifies the verb (calculate), resource (match score), and outputs, distinguishing it from siblings like search_schools or compare_to_applicants.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context on when to use the tool (given GPA/test score) and explicitly notes "Public — no authentication required," which signals accessibility. However, it does not explicitly contrast with alternatives (e.g., compare_to_applicants) or mention when not to use it, leaving some implicit guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.3/5.0
Disambiguation4/5

Each tool has a distinct primary purpose: searching for schools, retrieving detailed stats for a known school, and calculating match probability from applicant data. There is minor overlap because search_schools also returns admissions statistics, but the difference in focus (discovery vs. details vs. personalized analysis) is clear enough.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern with snake_case: calculate_match_score, get_school_stats, and search_schools. The verbs are specific and clearly indicate the action each tool performs.

Tool Count5/5

With 3 tools, the server is tightly scoped and each tool covers a necessary part of the admissions workflow: finding schools, getting statistics, and computing match scores. This is within the ideal 3-15 range and does not feel padded or sparse.

Completeness4/5

The server covers the core read-only admissions use cases: discovery, stats lookup, and personalized match calculation. A comparison feature or direct ranking list tool would be a nice addition, but agents can accomplish common tasks by combining search and stats with match scoring, so there are no critical dead ends.

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