List Student Quiz Accommodations

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

Beyond the readOnlyHint annotation, the description discloses significant behavioral details: it reads from submission records because Canvas exposes no direct read endpoint, makes one API call per Classic Quiz, may be slow for many quizzes, and propagates errors immediately without per-quiz catching. These details are valuable for an agent predicting latency, failure modes, and side effects. No contradiction with annotations.

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 composed of six tightly packed sentences, each serving a distinct purpose: what it lists, when to use it, what it excludes, how it reads, performance/error characteristics, and the pseudonym prerequisite. No filler or redundant elaboration; information is front-loaded with the core action first.

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?

With no output schema, the description covers the essential dimensions: scope, exclusions, implementation method, performance, error behavior, and a preprocessing requirement. It does not explicitly state the return shape (e.g., a list of quiz IDs with extra_time/extra_attempts), but the opening line and the reading mechanism imply it. For a read-only list tool with this complexity, the description is nearly complete, with only a small gap around response format.

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 already documents both parameters (course_id and user_id) with clear descriptions, and the schema description coverage is 100%. The description's phrase 'Provide user_id as the real Canvas user ID' reinforces the schema but adds little beyond it. Since the schema carries the semantic load, baseline 3 is appropriate.

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 opens with 'List the current quiz accommodation (extra time and/or extra attempts) for a specific student across all Classic Quizzes in a course,' using a specific verb, resource, and scope. It explicitly excludes New Quizzes, distinguishing it from list_student_new_quiz_accommodations and matching its title. This is a model of clear purpose.

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 states 'Useful for auditing before or after calling set_student_quiz_accommodation,' which gives actionable context for when to invoke it. It also provides a prerequisite for pseudonymized environments ('If CANVAS_PSEUDONYMIZE_STUDENTS is enabled, call resolve_pseudonym first') and notes the New Quizzes exclusion. It does not explicitly name alternative tools for New Quizzes, but the contrast with set_student_quiz_accommodation and the exclusion phrase make intended usage clear.

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