Skip to main content
Glama

canvas_submit_assignment

Submit an assignment in Canvas by specifying the course ID, assignment ID, and submission type (online text, URL, or file uploads) using the MCP server integration.

Instructions

Submit work for an assignment

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
assignment_idYesID of the assignment
bodyNoText content for text submissions
course_idYesID of the course
file_idsNoFile IDs for file submissions
submission_typeYesType of submission
urlNoURL for URL submissions
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. 'Submit work' implies a write/mutation operation, but the description doesn't mention permission requirements, whether submissions are final or can be edited, rate limits, or what happens on success/failure. For a mutation tool with zero annotation coverage, this leaves significant behavioral gaps.

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 wasted words. It's appropriately sized for a tool with good schema documentation and gets straight to the point without unnecessary elaboration.

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?

For a mutation tool with no annotations and no output schema, the description is insufficient. It doesn't explain what happens after submission (confirmation? grade?), error conditions, or important constraints. The combination of mutation behavior, multiple parameters, and lack of structured metadata requires more descriptive context than provided.

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 6 parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema (like explaining the relationship between submission_type and other parameters). Baseline 3 is appropriate when 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 clearly states the action ('Submit work') and target resource ('for an assignment'), providing a specific verb+resource combination. However, it doesn't distinguish this tool from potential siblings like 'canvas_get_submission' or 'canvas_submit_grade', which reduces clarity in the broader tool ecosystem.

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?

The description provides no guidance on when to use this tool versus alternatives. There's no mention of prerequisites (like needing to be enrolled in the course), timing constraints (submission deadlines), or how this differs from related tools like 'canvas_get_submission' (viewing submissions) or 'canvas_submit_grade' (grading submissions).

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