Canvas LMS MCP Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
The two tools serve clearly distinct purposes: one retrieves course enrollments with grades, the other retrieves upcoming assignments. There is no ambiguity in selecting between them.
Naming Consistency5/5Both tools follow a consistent 'list_' prefix followed by a clear noun phrase ('courses_and_grades', 'upcoming_assignments'), making the naming pattern uniform and predictable.
Tool Count3/5With only two tools, the server feels minimal but not entirely unreasonable for a focused student dashboard scope. The count is at the borderline of being thin, as agents may expect additional related operations.
Completeness3/5The tool surface covers the two primary student needs (grades and upcoming assignments) but lacks operations like retrieving individual course details, assignment submissions, or past assignmentscars. This leaves notable gaps for broader academic workflows, though it may suffice for a narrow use case.
Average 4.4/5 across 2 of 2 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 2 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds useful behavioral nuance beyond those: grading-period behavior, inclusion of the whole-course total alongside the in-progress score, and the 'scope' field for interpretation. This is valuable context for a read-only listing tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences. The first sentence front-loads the primary purpose, and the second adds an important grading-period nuance. There is no filler or repetition of schema details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only listing tool with one optional parameter, an output schema, and high schema coverage, the description is complete. It explains active enrollment, grading-period behavior, and term defaulting, which is sufficient for an agent to select and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the only parameter, include_all_terms, is well documented in the schema with a clear description and default value. The main description restates the default behavior ('Defaults to the current term only') but adds no additional parameter-level meaning, so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action ('Fetch every course the user is actively enrolled in as a student') and a clear resource ('courses... together with the current grade for each'). It also adds meaningful scope distinctions like 'Defaults to the current term only' and grading-period behavior, which clearly separates it from the sibling assignment-listing tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context for when to use the tool: when you need active course enrollments and current grades, defaulting to the current term. It does not explicitly mention alternatives or exclusions relative to list_upcoming_assignments, but the course/grade vs. assignment subject matter implies the boundary clearly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint=true and openWorldHint=true, so the description doesn't need to repeat those. It goes beyond the annotations by specifying the filtering logic (not submitted, not graded, not excused), sorting order (soonest first), and default inclusion of overdue items. This adds useful behavioral context without contradicting the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core purpose, and every clause adds value. It efficiently conveys the tool's scope, filtering criteria, ordering, and default behavior without any fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists (as noted in the context), the description doesn't need to explain return values. The tool is relatively simple with 4 optional parameters, and the description plus schema fully cover the behavior. It could be a 5, but the absence of any prerequisites or error conditions keeps it at a solid 4.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already covers 100% of the parameters with descriptions. The tool description adds a bit more by explaining the filtering and sorting behavior, which relates to how the parameters interact (e.g., include_overdue default true). Since schema coverage is high, a baseline 3 is appropriate, but the description's mention of 'sorted soonest first' and 'Past-due work ... included by default' adds context beyond the schema, justifying a 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Fetch') and identifies the resource ('assignments across the user's active courses') with clear criteria ('not submitted, not graded, not excused'). It also notes the sorting by due date and the inclusion of past-due unsubmitted work, which distinguishes it from the sibling tool (list_courses_and_grades) that focuses on grades and courses.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description makes it clear that the tool is for outstanding assignments, which implies using it when you need to see what's due or overdue. It does not explicitly state when NOT to use it or mention the sibling alternative, but the context is sufficiently clear for most use cases, and the default behavior (including overdue) is disclosed.
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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