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Fiveable for AP Teachers

Get grading rubric

get_grading_rubric

Returns the session's Fiveable scoring guidelines point by point on the AP rubric for its question type. If absent, starts rubric generation and returns pending status; generation takes about a minute. Rubrics use the website's grading pipeline. Cheap once written.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sessionIdYesGrading session id from list_grading_sessions or create_grading_session.

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.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Goes beyond the annotations by disclosing the real side effect (a missing rubric triggers generation), the returned pending status, the ~1 minute latency, and the cost profile ('cheap once written'). This also explains why readOnlyHint=false and idempotentHint=false are set, which the structured fields alone would leave ambiguous. It does not cover auth requirements or failure modes, so not a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Four short sentences, front-loaded with what is returned and immediately followed by the important non-obvious behavior. Phrasing like 'Cheap once written' and 'Fiveable scoring guidelines' is terse/jargon-y but each sentence carries information.

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?

With an output schema present, return-value detail is unnecessary, and the description covers the tricky asynchronous path (pending status plus generation time) that an agent must handle. Missing only failure/permission context for a session that is not yet gradeable.

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?

Only one parameter and schema description coverage is 100%, with the schema itself explaining the sessionId format and its origin (list_grading_sessions or create_grading_session). The description adds the notion that the rubric is scoped to the session's question type but nothing about the parameter itself, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Returns the session's ... scoring guidelines point by point on the AP rubric for its question type'), which is concrete enough to distinguish it from get_grading_results and get_grading_progress. It does not name the closest sibling, update_grading_rubric, or otherwise route the agent explicitly, so it stops short of a 5.

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?

Usage is implied rather than stated: fetch the rubric for a grading session, and the description notes the fallback when no rubric exists. There is no explicit when-to-use versus update_grading_rubric or get_grading_results, and no stated prerequisites such as needing an active grading session.

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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