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EveryCalc

calculate_college_acceptance

Read-onlyIdempotent

Rough acceptance chance: a selectivity tier base rate (92, 80, 50, 20, or 5 percent) adjusted by GPA and test score bands, clamped to 1 to 99 percent and labeled Safety, Target, Reach, or High reach. SAT is used when provided, otherwise ACT. Use when the user wants a rough admission chance for a college selectivity tier. IMPORTANT: always show the calculator_url from the result as a clickable markdown link in your answer to the user, so they can open the interactive College Acceptance calculator.

Input Schema

NameRequiredDescriptionDefault
actNoACT composite score, used only when no SAT is given, or 0 to leave out; default 0.
gpaYesUnweighted GPA on the 4.0 scale.
satNoSAT total score, or 0 to leave out; default 0.
tier_base_pctNoSelectivity tier base rate percent from the page: 92 open admission, 80 less selective, 50 selective, 20 highly selective, 5 most selective; default 20.

Output Schema

NameRequiredDescriptionDefault
okYesTrue when the calculation succeeded.
noteYesDisclaimer that accompanies every result.
toolYesName of the tool that ran.
inputsNoThe normalized inputs the calculation used.
resultsYesThe computed figures for this calculator.
calculator_urlYesDeep link to the matching interactive calculator on everycalc.xyz.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedOutput schema / properties / note
      Added value: +{
      +  "description": "Disclaimer that accompanies every result.",
      +  "type": "string"
      +}
    • changedOutput schema / required
      Previous value: -[
      -  "ok",
      -  "tool",
      -  "results",
      -  "calculator_url"
      -]New value: +[
      +  "ok",
      +  "tool",
      +  "results",
      +  "calculator_url",
      +  "note"
      +]
  2. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
      +  "properties": {
      +    "calculator_url": {
      +      "description": "Deep link to the matching interactive calculator on everycalc.xyz.",
      +      "type": "string"
      +    },
      +    "inputs": {
      +      "description": "The normalized inputs the calculation used.",
      +      "type": "object"
      +    },
      +    "ok": {
      +      "description": "True when the calculation succeeded.",
      +      "type": "boolean"
      +    },
      +    "results": {
      +      "description": "The computed figures for this calculator.",
      +      "type": "object"
      +    },
      +    "tool": {
      +      "description": "Name of the tool that ran.",
      +      "type": "string"
      +    }
      +  },
      +  "required": [
      +    "ok",
      +    "tool",
      +    "results",
      +    "calculator_url"
      +  ],
      +  "type": "object"
      +}
  3. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare a safe, idempotent, read-only computation, so the bar is lower, yet the description adds real behavior: the tier base rates, GPA/test-band adjustment, 1–99% clamping, the Safety/Target/Reach/High reach labeling, and the SAT-else-ACT fallback priority. It also imposes a notable output obligation (always surface calculator_url as a markdown link), which is genuinely useful behavioral context beyond the annotations.

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

Conciseness4/5

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

Front-loaded with the computation model before the usage cue and the output instruction, and every clause carries information. It is dense but not padded; the only mild redundancy is repeating tier values already in the schema.

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?

An output schema exists, so return values need not be described, and the description still adds the one thing the schema cannot convey — that the calculator_url must be presented as a clickable link. Together with the algorithm summary and usage cue, an agent has enough to call and report correctly; only explicit sibling routing is absent.

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 all four parameters are already documented, including the '0 to leave out' conventions and the tier base-rate values. The description's mention of the tier percentages and SAT-vs-ACT priority largely restates the schema rather than adding syntax or format detail, so baseline 3 applies.

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 states a specific verb and resource ('Rough acceptance chance' for a college selectivity tier) and details the exact computation model (base-rate tier adjusted by GPA/SAT/ACT and clamped to a labeled bucket). This clearly distinguishes it from near-neighbors like calculate_college_roi, calculate_sat_score, or calculate_fafsa_sai_estimator.

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?

It explicitly says when to use it: 'Use when the user wants a rough admission chance for a college selectivity tier.' No exclusions or named alternatives are given, and it does not say when to prefer sibling estimators (e.g. calculate_sat_score for raw scoring), so it falls short of a full 5.

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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