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Glama

Fiveable for AP Students

Answer the current diagnostic question

submit_diagnostic_answer
Idempotent

Saves one answer in the connected student's active Fiveable diagnostic and returns feedback plus exactly one next adaptive question. Accepts the option letter, full text, or answer id from the current diagnostic question. Handles one question per call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
answerYes
intentNoPrivacy-safe reason for the request. Use explain, quiz, notes, frq, or research; never send the student's raw prompt for analytics.
requestIdNoReuse this ID for retries of the same operation; changed input returns a conflict.
questionIdYes
sessionTokenYes

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
Behavior4/5

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

The annotations already indicate mutation is expected (readOnlyHint=false) and idempotency is supported. The description adds useful behavioral context: it saves exactly one answer, returns feedback plus one adaptive next question, and accepts multiple answer formats. This goes beyond the annotations without contradicting them.

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 tightly written sentences front-load the core action and result, then add input-format and batching constraints. There is no redundant or filler content.

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 one-shot diagnostic answer submission, the description covers what is saved, what is returned, accepted input forms, and the per-call limit. Combined with the schema descriptions for requestId and intent, the tool definition is complete enough for an agent to select and invoke it correctly.

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 only 40%, so the description partially compensates by clarifying the answer parameter can be an option letter, full text, or answer id. The remaining parameters (sessionToken, questionId) are self-explanatory or already described in the schema, and the 'one question per call' note adds scope clarity.

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 states a specific action ('Saves one answer'), names the exact resource ('active Fiveable diagnostic'), and describes the return behavior ('feedback plus exactly one next adaptive question'). This clearly distinguishes the tool from sibling tools like submit_practice_answer and check_practice_answer.

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 gives clear context for when this tool is appropriate: answering the current question in the student's active diagnostic. It emphasizes 'one question per call' and the diagnostic-specific scope, though it does not explicitly name alternatives or state 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.