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Fiveable for AP Students

List key terms

list_key_terms
Read-onlyIdempotent

Lists the key vocabulary terms for an AP subject, paginated alphabetically. Suitable for building flashcards or checking what vocabulary a student should know; results include stable term slugs for full definitions. Cheap.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageYes1-indexed page number.
limitYesTerms per page.
intentNoPrivacy-safe reason for the request. Use explain, quiz, notes, frq, or research; never send the student's raw prompt for analytics.
letterYesFilter to terms starting with one letter, or "ALL" for every term.ALL
subjectSlugYesFiveable subject slug, e.g. "ap-bio" or "apush". Use list_subjects to find it.

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

A4.2/5.0
Behavior4/5

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

Annotations already cover read-only, idempotent, non-destructive behavior, so the description adds useful extras: pagination is alphabetical, results contain stable slugs, and the call is 'Cheap' from a cost/performance standpoint. No contradiction 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?

Two tight sentences deliver the core behavior, use cases, result relevance, and a performance note with zero fluff. The primary action is front-loaded, and the 'Cheap' note earns its place as operational guidance.

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?

Given an output schema, annotations, and full parameter descriptions, the description covers the essential behavioral and usage context. It could be slightly stronger by explicitly naming get_key_term as the follow-up for full definitions, but the slug reference makes that connection reasonably clear.

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 explains page, limit, letter, and subjectSlug. The description's 'paginated alphabetically' adds ordering context, but it does not add meaningful parameter-level meaning 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?

States the exact action ('Lists'), specific resource ('key vocabulary terms'), and scope ('for an AP subject'), with pagination and alphabetical ordering. It also distinguishes itself from the sibling get_key_term by noting that results include stable term slugs for full definitions.

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?

Provides clear use cases: building flashcards and checking expected vocabulary, which helps an agent decide when listing is appropriate. It implies get_key_term for full definitions via 'stable term slugs' but does not explicitly name alternatives or say when not to use this tool.

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.