Canvas MCP Server

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 describes what the tool returns (system-wide announcements with examples) but lacks critical behavioral details such as whether it requires authentication, how results are paginated or sorted, if there are rate limits, or what the return format looks like (e.g., JSON structure). For a read operation 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 appropriately sized and front-loaded: the first sentence states the core purpose, and subsequent sentences provide clarifying details without redundancy. Every sentence earns its place by adding context about the type of announcements returned. There is zero waste, making it highly 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's complexity (simple read operation with 0 parameters) and lack of annotations and output schema, the description is partially complete. It adequately explains what the tool does and the scope of data, but it misses behavioral aspects like authentication needs, response format, or pagination. For a tool with no structured fields to rely on, it should do more to be fully helpful to an AI agent.

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, meaning no parameters are documented in the schema. The description doesn't mention any parameters, which is appropriate since none exist. It adds value by explaining the scope of data returned (system-wide announcements from your institution), which compensates for the lack of parameter documentation. Baseline is 4 for 0 parameters as per rules.

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 as 'List account notifications/announcements' with a specific verb ('List') and resource ('account notifications/announcements'). It distinguishes itself from siblings like canvas_announcement_list by specifying it returns 'system-wide announcements from your institution' rather than course-specific announcements. However, it doesn't explicitly contrast with canvas_inbox_list or canvas_todo_list, which might also involve notifications.

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 implies usage context by stating it returns 'system-wide announcements from your institution' with examples like 'Important notices, System maintenance alerts, Policy updates.' This suggests it should be used for institutional-level notifications rather than course-specific ones. However, it doesn't explicitly state when not to use it or name alternatives like canvas_announcement_list for course announcements, leaving some ambiguity.

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