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bruchris

Canvas LMS MCP Server

by bruchris
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TypeScript
Hybrid

project_grade

Read-only

Project the minimum percentage required on remaining assignments to reach a target course grade. Accounts for assignment weights, drop rules, and grading schemes.

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

A4.6/5.0
Behavior5/5

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

Annotations (readOnlyHint=true, openWorldHint=true, destructiveHint=false) are complemented by detailed behavioral disclosures: uniform-x assumption, drop rules frozen, late penalties not factored, one student per call, pseudonym handling. No contradiction with annotations.

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?

Well-structured: concise summary, then detailed explanation, then limitations, then V1 specifics. Front-loaded with purpose. Approximately 150 words, efficient. Could be slightly tighter but no wasted 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?

Despite no output schema, description explains return fields (minimum_pct, feasibility, per-group breakdown, summary). Covers complexity of grade projection with weights, drops, and grading standards. Includes limitations and pseudonym handling. Complete for an AI to select and invoke correctly.

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 100% with descriptions. Description adds meaning beyond schema by explaining mutual exclusivity of target_letter/target_percentage, implicit self-computation when student_id omitted, and dependency on grading standard for letter targets. Provides context for how parameters are used in projection logic.

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?

Description clearly states the tool projects minimum score needed on remaining assignments to reach a target grade. Uses specific verb ('projects') and resource ('scores/grades'). Distinct from siblings, none of which perform grade projection.

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?

Includes limitations (uniform-x model, frozen drop rules, no late penalties) that guide appropriate usage. Specifies output for self vs. other students via student_id parameter. Could explicitly state when not to use (e.g., per-item optimization needed), but limitations effectively serve as guidance.

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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