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Fiveable for AP Students

List free-response questions

list_frqs
Read-onlyIdempotent

Lists Fiveable's free-response (FRQ) practice prompts for an AP subject, optionally filtered by FRQ type, unit, and excluded IDs. Results use stable ID order; unchanged filters continue pagination. Returns prompt ids and summaries rather than full prompts. Cheap.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitYesMaximum prompts to list.
cursorNo
intentNoPrivacy-safe reason for the request. Use explain, quiz, notes, frq, or research; never send the student's raw prompt for analytics.
unitIdNoUnit ID or public unit slug from get_subject_outline.
frqTypeNoOptional FRQ type filter, e.g. "DBQ", "LEQ", "SAQ". Varies by subject.
excludeIdsNo
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

A4.5/5.0
Behavior5/5

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

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds valuable behavioral context beyond annotations: stable ID ordering, pagination semantics with unchanged filters, the fact that only summaries are returned, and the cost characteristic ('Cheap'). This helps an agent reason about repeated calls and performance.

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 four tight sentences with no filler. It front-loads the core purpose, then adds pagination, return-content, and cost details that each earn their place. The one-word 'Cheap' is concise but informative.

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?

Given the tool has an output schema, the description does not need to detail return fields. It sufficiently covers filtering, pagination, content scope, and cost, making it complete enough for an agent to select and invoke the tool correctly without missing critical behavioral expectations.

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 description coverage is 71%, so the schema covers most parameter meanings. The description adds useful semantics by naming the filter dimensions (FRQ type, unit, excluded IDs) and explaining pagination behavior tied to cursor usage, complementing the schema without fully re-documenting every parameter.

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 clearly states the action ('Lists'), the resource ('Fiveable's free-response practice prompts'), and the scope ('for an AP subject'). It also distinguishes itself from the sibling get_frq tool by explicitly noting it returns prompt ids and summaries rather than full prompts.

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

Usage Guidelines3/5

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

The description implies when the tool is useful: listing filtered FRQ prompts in paginated form. However, it does not explicitly name an alternative like get_frq for full-prompt needs or state when not to use this tool, so the guidance remains implicit rather than explicit.

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.