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bruchris

Canvas LMS MCP Server

by bruchris
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TypeScript
Hybrid

Project Grade

project_grade
Read-only

Determine the minimum percentage needed on remaining assignments to reach a target course grade, accounting for assignment weights and drop rules.

Instructions

Projects the minimum score needed on remaining assignments to reach a target course grade.

Given a target (as a percentage, e.g. 90, or a letter grade, e.g. "A") and the student's current scores, computes the minimum uniform percentage that must be earned on all remaining (not yet graded) assignments for the overall course grade to reach the target. Accounts for assignment-group weights, drop_lowest / drop_highest / never_drop rules, and the course grading scheme (for letter-grade targets and output letter mapping).

Returns:

  • minimum_pct_on_remaining: the uniform percentage needed on all remaining items.

  • feasibility: 'achievable' | 'already_secured' | 'impossible'.

  • Per-group breakdown of locked-in scores and remaining assignments.

  • A plain-language summary.

Limitations:

  • Uses a uniform-x model: the same percentage is assumed for every remaining item. This is the natural interpretation of "minimum average needed." Per-item optimization is not supported.

  • Drop rules are frozen at their current state (based on already-graded scores); which items are dropped may shift as remaining assignments are graded.

  • Late-submission penalties are not factored in.

  • V1 computes one student per call. Omit student_id to compute for the authenticated user. When CANVAS_PSEUDONYMIZE_STUDENTS is enabled, resolve the pseudonym first via resolve_pseudonym.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
course_idYesCanvas course ID to compute the grade projection for.
student_idNoCanvas user_id of the student to compute for. Omit to compute for the authenticated user. Instructors may pass any enrolled student's user_id. When CANVAS_PSEUDONYMIZE_STUDENTS is enabled, pass the numeric Canvas user_id after resolving the pseudonym via resolve_pseudonym.
target_letterNoTarget course grade as a letter (e.g. "A", "B+"). Requires the course to have a grading standard configured. Exactly one of target_percentage or target_letter must be provided. Case-insensitive.
target_percentageNoTarget course grade as a percentage (0–100). Exactly one of target_percentage or target_letter must be provided. Example: 90.0 for a 90% target.

Tool Definition Quality

A4.5/5.0
Behavior5/5

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

Goes well beyond the readOnlyHint/destructiveHint annotations by disclosing the uniform-x model, frozen drop rules, unsupported late penalties, per-student limitation, and pseudonym resolution requirement. This gives the agent a strong understanding of the tool's edge cases and limitations.

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 well-structured with a lead summary, a Returns section, and a Limitations section. It is appropriately detailed for the tool's complexity and contains no redundant or filler sentences.

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?

Even without an output schema, the description fully enumerates return values, feasibility states, per-group breakdown, and plain-language output. It also covers key constraints and environmental considerations, making it complete for a complex projection tool.

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?

Schema description coverage is 100%, so the schema already documents all four parameters well. The description adds minor context such as the exclusivity of target types and pseudonym handling, but it does not significantly extend the parameter semantics beyond the schema.

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 opens with a specific verb and resource: 'Projects the minimum score needed on remaining assignments to reach a target course grade.' It clearly differentiates this computation tool from sibling tools like explain_grade and explain_grading_policy by focusing on projection rather than explanation.

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?

Provides clear invocation context: when to use it, how targets work, and how to handle student_id including pseudonym resolution. However, it does not explicitly mention alternatives or state when NOT to use this tool versus sibling tools.

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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