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create_rubric

Create rubric criteria for a course and optionally associate with an assignment. Define custom rating levels and point values using JSON.

Instructions

Create a new rubric in a course, optionally associating it with an assignment.

    Uses bracket-notation form-data encoding required by the Canvas rubric API.

    The ``criteria`` parameter is a JSON string mapping arbitrary criterion keys to
    objects with the following fields:

    - ``description`` (required): Short criterion label shown in the rubric grid
    - ``points`` (required): Maximum points for this criterion (non-negative number)
    - ``long_description`` (optional): Detailed criterion description
    - ``ratings`` (optional): List (or object) of rating levels, each with:
        - ``description`` (required): Rating label (e.g. "Excellent")
        - ``points`` (required): Points for this rating (non-negative number)
        - ``long_description`` (optional): Detailed rating description

    Example ``criteria`` JSON::

        {
          "c1": {
            "description": "Content Quality",
            "points": 10,
            "ratings": [
              {"description": "Excellent", "points": 10},
              {"description": "Satisfactory", "points": 7},
              {"description": "Needs Work", "points": 3}
            ]
          },
          "c2": {
            "description": "Grammar",
            "points": 5,
            "ratings": [
              {"description": "No errors", "points": 5},
              {"description": "Minor errors", "points": 3}
            ]
          }
        }

    Args:
        course_identifier: Course code or Canvas ID
        title: Rubric title
        criteria: JSON string defining rubric criteria (see docstring above)
        assignment_id: Optional assignment ID to immediately associate the rubric with
        use_for_grading: When associating with an assignment, use rubric for grade
                         calculation (default: False)
        reusable: Make rubric reusable across courses (default: False)
        free_form_criterion_comments: Allow free-form comments per criterion
                                      instead of rating selection (default: False)
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
course_identifierYes
titleYes
criteriaYes
assignment_idNo
use_for_gradingNo
reusableNo
free_form_criterion_commentsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations provided, the description must convey behavioral traits. It explains the bracket-notation form-data encoding requirement and details the criteria parameter structure. However, it omits important information such as required permissions, idempotency, response format (despite output schema existing but undocumented), and potential side effects. The description adds value but leaves gaps.

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

Conciseness3/5

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

The description is well-structured with a clear intro, encoding note, criteria documentation, and parameter list. However, it is lengthy due to the extensive criteria example and technical encoding details. While every part is relevant, it could be more concise; the criteria explanation could be truncated and referenced in the parameter description section.

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 (7 parameters, JSON criteria), the description provides thorough coverage of inputs, especially the critical criteria structure. However, it does not describe the output or return value (despite an output schema existing), and lacks example calls or usage patterns. It is mostly complete for input but not fully self-contained.

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 must compensate. It does so excellently by thoroughly explaining each parameter, especially the 'criteria' parameter with a detailed docstring and a JSON example. It adds meaning beyond the schema's type and title fields for all 7 parameters.

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 'Create a new rubric in a course, optionally associating it with an assignment.' This is a specific verb-resource pair with scope, effectively distinguishing it from sibling tools like 'associate_rubric' and 'list_rubrics'.

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 to use the tool (to create a rubric) and mentions optional association with an assignment, but does not explicitly state when not to use it or contrast it with alternatives like 'associate_rubric' for linking existing rubrics. Usage context is clear but lacks exclusions.

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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