Skip to main content
Glama

propose_rubric

Proposes a rubric for instructor approval by reading the assignment instruction sheet, existing Canvas rubric, or falling back to a suggested default.

Instructions

The rubric the engine reads for this assignment, for the instructor to approve.

Priority is the instruction sheet first, then the Canvas rubric object, then the default — a proposal, never an applied decision.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYes
courseYes
graderNo
Behavior3/5

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

With no annotations, the description carries the full burden. It clearly states the tool creates a proposal, never an applied decision, which is a key behavioral trait. However, it does not disclose side effects, whether it saves data, what happens after approval, or permission requirements. The priority order adds useful context but is incomplete.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is brief but the first sentence is an awkward noun phrase ('The rubric the engine reads...') rather than a clear action statement. It is concise, but the structure is not optimally front-loaded for an agent to quickly grasp the tool's purpose. The second sentence clarifies, but the opening could be more direct.

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?

The text covers the proposal nature and priority order, but for a tool with 3 undocumented parameters and no output schema, it leaves many gaps: what the tool actually does with the inputs, what the response looks like, how it integrates with the assignment workflow, and any side effects. It is only minimally complete for an agent to safely invoke.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% and the description provides no explanations for the parameters course, code, or grader. The phrase 'for this assignment' hints at context but does not define any parameter meaning. The description fails to compensate for the lack of schema descriptions.

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 implies the tool proposes a rubric for instructor approval by stating 'a proposal, never an applied decision' and mentions the instructor's approval. However, it lacks an explicit action verb like 'proposes' or 'creates', relying on the second sentence for clarity. It does distinguish from siblings like get_rubric by indicating this is a proposal, not a retrieval.

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 explicit guidance on when to use this tool versus alternatives. The priority statement explains rubric sources (instruction sheet, Canvas rubric, default) but not usage conditions. It does not mention prerequisites such as needing an existing assignment or that get_rubric should be used for inspecting existing rubrics.

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

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/Canvas-LMS-MCP/canvas-teacher-mcp'

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