Exam Grades and Entry

WASSCE aggregate for Ghanaian universities

ghana.wassce_aggregate

Read-only

Use this when a student in Ghana gives their WASSCE grades and asks for their aggregate, or whether the grades meet a university's general entry requirement (for example "I have English B3, Core Maths C4, Science B2, Social A1 and electives B2, B3, C5: what is my aggregate?"). It counts the core subjects each route uses and the three strongest electives, and checks the credits and the aggregate of 24 that the University of Ghana and KNUST publish. Do not use it to predict a programme's cut-off, for grades from another examination, or to answer exam questions.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "english": "<english>",
      "core_mathematics": "<core_mathematics>",
      "electives": "<electives>"
    },
    "name": "ghana.wassce_aggregate"
  }
}

Input Schema

Name	Required	Description
`english`	Yes	Grade in English Language.
`electives`	Yes	The grades in elective subjects, with or without the subject names, for example "Physics B3, Chemistry C4, Biology B2, Elective Maths C6". At least three.
`social_studies`	No	Grade in Social Studies. Leave out if not known.
`core_mathematics`	Yes	Grade in Core Mathematics.
`integrated_science`	No	Grade in Integrated Science. Leave out if not known.

Output Schema

Name	Required	Description	Default
`exam`	Yes
`note`	Yes
`read_on`	Yes
`sources`	Yes
`explore_url`	No
`how_counted`	Yes
`universities`	Yes
`grade_meanings`	No
`aggregate_science`	Yes
`electives_counted`	Yes
`aggregate_non_science`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 10, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Adds real behavior beyond the annotations: it counts route-specific core subjects and the three strongest electives, and enforces credit requirements plus the aggregate-24 threshold used by UG and KNUST. It stops short of naming which core subjects apply to which route, so the computation rule is partially disclosed rather than fully.

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 trigger condition and a concrete example, then the exclusions. The embedded example is long but replaces several sentences of explanation; only the repeated grade string is slightly wasteful.

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?

With annotations covering safety, a full schema and an output schema, the description only needs usage, computation and limits, and it supplies all three. The remaining gap is which core subjects each route uses, useful but obtainable from the return value.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3. The description earns above baseline by explaining how the free-text electives parameter is consumed (three strongest counted) and that core-subject participation depends on the route, which the schema does not convey.

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?

States a specific verb and resource: compute a WASSCE aggregate and check a Ghanaian university's general entry requirement. The worked example ("English B3, Core Maths C4...") pins down the input shape, and the exclusion of cut-off prediction separates it from a different kind of calculation.

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

Usage Guidelines5/5

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

Explicit when-to-use with a quoted student question, plus explicit when-not-to-use: cut-off prediction, other examinations, exam questions. Alternatives are framed by condition rather than by sibling name, which is enough to route an agent.

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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