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Fiveable for AP Students

by io.github.thinkfiveable

Predict an AP score

calculate_ap_score
Read-onlyIdempotent

Predicts a 1-5 AP score from raw section scores using Fiveable's per-subject score calculator, and explains the weighting. Call with no sectionScores to see which sections the subject has and their point maximums, then call again with the student's scores. Cheap.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
intentNoPrivacy-safe reason for the request. Use explain, quiz, notes, frq, or research; never send the student's raw prompt for analytics.
subjectSlugYesFiveable subject slug, e.g. "ap-bio".
sectionScoresNoRaw scores per card. Omit to discover the subject's card structure first.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
textNoReadable tool result text for clients that consume structured output.
statusYesOperation status: completed, pending, partial, failed, unavailable, or a domain-specific outcome.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds useful behavioral context beyond this: it explains that the tool also describes the weighting, that omitting sectionScores lets users discover section point maximums, and that the operation is computationally cheap. No contradictions with annotations.

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 two concise sentences with no filler. It front-loads the core function, then immediately gives the essential usage pattern, and closes with a one-word cost signal. Every sentence earns its place.

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

Completeness5/5

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

For a read-only, idempotent calculator with a full output schema and 100% parameter coverage, the description is complete. It explains the discovery-then-predict loop, the weighting explanation, and cost characteristics. Nothing needed to correctly invoke the tool is missing.

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 schema already documents subjectSlug, intent, and sectionScores thoroughly. The description adds a useful high-level workflow for sectionScores ('Call with no sectionScores to see which sections the subject has'), but this mostly mirrors the schema's own guidance to 'Omit to discover the subject's card structure first.' Thus the description adds only marginal value beyond the schema.

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 opens with a specific verb and resource: 'Predicts a 1-5 AP score from raw section scores using Fiveable's per-subject score calculator.' This clearly distinguishes the tool from siblings like score_frq_response or get_scoring_result, which handle different scoring workflows. The title 'Predict an AP score' reinforces a singular, unambiguous purpose.

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 an explicit two-step workflow: call with no sectionScores to discover the section structure, then call again with actual scores. This is strong usage guidance, though it does not name alternative tools or conditions for choosing a sibling, so it stops short of full exclusion 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

A3.9/5.0
Disambiguation4/5

Most tools target clearly distinct resources and actions, but a few close pairs exist, such as check_practice_answer vs. submit_practice_answer and get_content_sections vs. get_study_guide. Descriptions clarify the boundaries, yet the sheer number of similar get_* and list_* tools adds some selection risk.

Naming Consistency5/5

All tool names follow a consistent snake_case verb_noun pattern, with verbs like get_, list_, create_, submit_, score_, check_, and update_. The get_my_* and list_my_* conventions for user-specific data are also applied predictably.

Tool Count2/5

With 37 tools, the server is well above the 25-tool threshold for a coherent surface and will be heavy for an agent to navigate. The broad platform scope explains some of the count, but many tools could be consolidated or grouped without losing capability.

Completeness4/5

The tool set covers the major student workflows: content study, MCQ practice, FRQ scoring, diagnostics, study plans, key terms, cheatsheets, exams, assignments, and progress tracking. Minor gaps exist, such as no study plan deletion and no MCP-based exam or assignment submission, but these appear to be deliberate platform boundary limitations.

Resources