Skip to main content
Glama

canvas_mark_module_item_complete

Mark a specific module item as complete within Canvas LMS by specifying course, module, and item IDs.

Instructions

Mark a module item as complete

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
course_idYesID of the course
item_idYesID of the module item
module_idYesID of the module
Behavior2/5

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

No annotations are provided, so the description carries full burden. 'Mark as complete' implies a write/mutation operation, but it doesn't disclose behavioral traits like required permissions, whether this is reversible, side effects (e.g., notifications, grade updates), rate limits, or what happens on success/failure. The description is minimal and lacks crucial context for safe invocation.

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 a single, efficient sentence with zero waste. It's front-loaded with the core action and resource, making it easy to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given a mutation tool with no annotations and no output schema, the description is incomplete. It lacks behavioral context (e.g., permissions, side effects), usage guidance, and details on return values or errors. For a tool that modifies state, this minimal description leaves significant gaps for an AI agent.

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%, with clear descriptions for course_id, item_id, and module_id. The description adds no parameter-specific information beyond what the schema provides, such as how these IDs relate or where to find them. Baseline 3 is appropriate since the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Mark a module item as complete' clearly states the action (mark as complete) and target resource (module item). It distinguishes from siblings like 'canvas_get_module_item' (read) and 'canvas_list_module_items' (list), but doesn't explicitly differentiate from potential completion-related tools that might exist.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a module item in an incomplete state), when not to use it, or what alternatives exist for checking completion status (like 'canvas_get_module_item').

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

Install Server

Other Tools

Related Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/DMontgomery40/mcp-canvas-lms'

If you have feedback or need assistance with the MCP directory API, please join our Discord server