canvas_grades_overview

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions what the tool returns (current grades, final grades, GPA help), but it doesn't cover critical behavioral traits such as whether this is a read-only operation, if it requires authentication, any rate limits, or how data is formatted (e.g., pagination, error handling). For a tool with zero annotation coverage, this is a significant gap in transparency.

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

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

The description is concise and well-structured, using a single sentence for the core purpose and a bulleted list for returns. Every sentence earns its place by providing essential information without fluff. However, it could be slightly more front-loaded by integrating the return details into the initial sentence for even better clarity.

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

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

Given the tool's complexity (simple read operation with no parameters) and the absence of annotations and output schema, the description is moderately complete. It explains what the tool does and what it returns, but it lacks details on behavioral aspects like authentication needs or error handling. For a tool with no structured fields to rely on, this leaves some gaps in contextual understanding.

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

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

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, focusing instead on the tool's output. This meets the baseline for tools with no parameters, as it avoids unnecessary repetition and adds value by explaining the return data.

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

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

The description clearly states the tool's purpose: 'Get an overview of your grades across all courses.' It uses a specific verb ('Get') and resource ('grades across all courses'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'canvas_get_my_submission' or 'canvas_missing_submissions' that might also relate to grades, which prevents a perfect score.

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

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. It doesn't mention any prerequisites, context for usage, or comparisons to sibling tools such as 'canvas_get_my_submission' for individual submissions or 'canvas_missing_submissions' for incomplete work. This lack of explicit when-to-use information limits its helpfulness for an AI agent.

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