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Fiveable for AP Students

by io.github.thinkfiveable

Get my study progress

get_my_study_progress
Read-onlyIdempotent

Returns the signed-in student's durable Fiveable progress for one subject: multiple-choice accuracy, weak topics and units, FRQ performance, skill groups and recent activity, respecting the website progress reset. Also includes completed exams, plus assignment and study-plan summaries when their separate permissions are granted. Call this before choosing what to explain or practice so recommendations carry across conversations and AI clients.

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".

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 provide readOnlyHint, idempotentHint, and destructiveHint. The description adds useful behavioral context: progress is 'durable' but 'respecting the website progress reset,' and assignment/study-plan summaries are only included 'when their separate permissions are granted.'

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?

Three sentences, each earning its place: core return data, permission-gated additions, and a clear usage directive. No filler or redundancy.

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?

With an output schema present, return-value details are not the description's job. It covers what the progress includes, durability/reset behavior, permission dependencies, and when to call it, which is complete for a read-only progress tool.

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 fully documents subjectSlug and intent. The description reinforces the 'one subject' scope and mentions permission-gated summaries, but adds little semantic value beyond the schema's own parameter descriptions.

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 ('Returns') and resource ('the signed-in student's durable Fiveable progress for one subject') plus the exact data included. It is clearly distinguished from siblings like get_my_practice_history and get_my_frq_progress by focusing on the aggregate progress for one subject.

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?

Explicitly says 'Call this before choosing what to explain or practice so recommendations carry across conversations and AI clients.' It gives clear when-to-use guidance, but does not name alternatives or explicitly say when not to use it.

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