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bulk_grade_submissions

Grade multiple student submissions in batches using rubric or point-based grading. Supports dry run validation and concurrent processing.

Instructions

Grade multiple submissions efficiently with concurrent processing.

    Supports both rubric-based and simple point-based grading in batches.

    Args:
        course_identifier: Course code or Canvas ID
        assignment_id: Canvas assignment ID
        grades: Dict mapping user_id to {rubric_assessment?, grade?, comment?}
        dry_run: If True, validate without submitting (default: False)
        max_concurrent: Max concurrent grading operations (default: 5)
        rate_limit_delay: Delay between batches in seconds (default: 1.0)
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
course_identifierYes
assignment_idYes
gradesYes
dry_runNo
max_concurrentNo
rate_limit_delayNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description must convey behavior. It mentions concurrent processing and dry-run validation, but does not disclose potential side effects, required permissions, or whether grades are immediately saved. The presence of an output schema partially compensates, but more behavioral context would be beneficial.

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 concise: a one-line summary followed by a well-structured Args block. Every sentence serves a purpose, no redundant information, and it is front-loaded with the key purpose.

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 the tool's complexity (6 parameters, nested objects, concurrency), the description covers core aspects: purpose, parameters, and concurrency controls. It lacks details on error handling, permissions, and confirmation of submission success, but the output schema likely addresses return values. Overall, it is fairly complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description's detailed Args section is essential. It explains each parameter's meaning, types, defaults, and the structure of the 'grades' object. This provides significant added value beyond the schema, making parameter semantics exceptionally clear.

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 explicitly states 'Grade multiple submissions efficiently with concurrent processing' and details both rubric-based and simple point-based grading. This clearly identifies the action (grade) and resource (multiple submissions), distinguishing it from single-grading tools like 'grade_with_rubric'.

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 implies batch usage through its name and mention of concurrent processing, but does not explicitly state when to use this tool versus alternatives like single-submission grading. It provides clear context for batch grading but lacks direct exclusions or alternative recommendations.

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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