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Fiveable for AP Teachers

Create grading session

create_grading_session

Creates a workspace grading session for finished student work, including paper, Google Docs or an AP Classroom export. Accepts a Fiveable FRQ or SAQ id, or the teacher's own question in prompt. A session uses an included assignment when grading starts unless the account has unlimited assignments. Returns the new session id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleNo
promptNoThe teacher's own question, when not a Fiveable one.
requestIdYesA unique id you generate for this action. Reuse it only when retrying the same action.
subjectSlugYese.g. apush or ap-bio.
fiveableQuestionIdNofrq:… or saq:… from search_question_bank, or frq: plus the FRQ id from get_frq.

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

A3.6/5.0
Behavior3/5

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

Annotations already cover the mutation/safety profile (readOnlyHint=false, destructiveHint=false, idempotentHint=false). The description adds one genuine behavioral trait beyond them: assignment consumption ('uses an included assignment when grading starts unless the account has unlimited assignments'). It adds little else about permissions or side effects, so a middling score is appropriate.

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?

Three tight sentences with the core purpose front-loaded, followed by input modes and quota/return notes. No filler, though the mention of return id partly duplicates the output schema.

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?

For a tool with a nested prompt object and an existing output schema, the description adequately covers purpose, accepted input forms, quota behavior, and return. The main gap is that it does not position this step relative to start_grading or the import tools.

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 coverage is 80%, so the baseline is 3. The description goes further by clarifying the mutual exclusivity of the two question sources ('Accepts a Fiveable FRQ or SAQ id, or the teacher's own question in prompt'), which the schema alone does not state, adding real meaning to fiveableQuestionId vs prompt.

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 (Creates) and resource (grading session) with scope qualifiers ('for finished student work, including paper, Google Docs or an AP Classroom export'). It implicitly separates itself from start_grading by noting the session is used 'when grading starts,' though it never names the sibling explicitly.

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?

Context is implied ('for finished student work') but the description gives no explicit when-to-use versus alternatives such as import_essays_from_files or start_grading. The agent must infer that this is the prerequisite step before grading begins.

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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