Canvas MCP Server

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('Reply to') but doesn't describe what happens upon invocation—e.g., whether the reply is immediate, if it requires moderation, what the response looks like, or any side effects like notifications. For a mutation 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 front-loaded with the core purpose in the first sentence, followed by a structured parameter list. Each parameter explanation is brief and relevant. There's no wasted text, though the formatting as a docstring with 'Args:' is slightly verbose but still efficient.

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 has an output schema (not provided here but indicated in context signals), the description doesn't need to explain return values. However, for a mutation tool with no annotations and 4 parameters, it lacks details on behavioral outcomes, error conditions, or usage context. The parameter explanations help, but overall completeness is minimal viable.

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?

Schema description coverage is 0%, so the description must compensate. It lists all four parameters with brief explanations (e.g., 'The Canvas course code' for course_identifier), adding meaning beyond the schema's generic titles. However, it doesn't provide format details (e.g., what 'entry_id' looks like) or constraints, leaving some ambiguity. This meets the baseline for partial compensation.

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 action ('Reply to') and target ('a student's discussion entry/comment'), making the purpose evident. It distinguishes from siblings like 'post_discussion_entry' (which creates new entries) by specifying this is a reply to an existing entry. However, it doesn't explicitly contrast with other reply-related tools if any existed.

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 prerequisites (e.g., needing proper permissions), when not to use it, or how it differs from similar tools like 'post_discussion_entry' beyond the basic action. The agent must infer usage from the name and parameters alone.

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