Skip to main content
Glama
bruchris

Canvas LMS MCP Server

by bruchris
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

Project Grade

project_grade
Read-only

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

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

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.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed8 schema fields changedv1.18.15
    • addedInput schema / properties / course_id / anyOf
      Added value: +[
+  {
+    "exclusiveMinimum": 0,
+    "maximum": 9007199254740991,
+    "type": "integer"
+  },
+  {
+    "pattern": "^[1-9][0-9]{0,18}$",
+    "type": "string"
+  }
+]
    • removedInput schema / properties / course_id / exclusiveMinimum
      Removed value: -0
    • removedInput schema / properties / course_id / maximum
      Removed value: -9007199254740991
    • removedInput schema / properties / course_id / type
      Removed value: -"integer"
    • addedInput schema / properties / student_id / anyOf
      Added value: +[
+  {
+    "exclusiveMinimum": 0,
+    "maximum": 9007199254740991,
+    "type": "integer"
+  },
+  {
+    "pattern": "^[1-9][0-9]{0,18}$",
+    "type": "string"
+  }
+]
    • removedInput schema / properties / student_id / exclusiveMinimum
      Removed value: -0
    • removedInput schema / properties / student_id / maximum
      Removed value: -9007199254740991
    • removedInput schema / properties / student_id / type
      Removed value: -"integer"
  2. Changed1 schema field changedv1.18.11
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  3. Addedv1.18.3

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds real behavioral depth beyond that: the uniform-x model assumption, frozen drop-rule state, exclusion of late penalties, and the pseudonymization prerequisite via resolve_pseudonym.

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?

It is long but well-structured and front-loaded: purpose first, then Returns, then Limitations. Every section earns its place, though the Returns block is verbose enough that a slightly tighter list would read better.

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?

With no output schema, the description carries the burden of describing return values, and it does so concretely (minimum_pct_on_remaining, feasibility enum, per-group breakdown, summary). Combined with the explicit limitations, an agent has everything needed to call and interpret the tool correctly.

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 course_id, student_id, target_letter, and target_percentage, including the exactly-one-of constraint. The description reinforces the target's dual form but adds no syntax or format detail beyond the schema, so the baseline 3 applies.

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+resource: 'Projects the minimum score needed on remaining assignments to reach a target course grade.' It clearly distinguishes itself from nearby grade tools (get_my_grades, explain_grade, get_gradebook_history_day) by being a forward-looking projection rather than a retrieval or 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?

It gives clear context for use ('Given a target ... and the student's current scores'), states the one-student-per-call scope, explains the omit-student_id default, and routes to resolve_pseudonym when pseudonymization is on. It does not explicitly say when to prefer this over siblings like explain_grade, so it stops short of full when/when-not/alternatives guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools