tool_get_answer_groups

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

With no annotations provided, the description carries full burden. It discloses that this is a read operation (listing/showing clusters) and mentions the efficiency benefit of batch grading. However, it doesn't cover important behavioral aspects like authentication requirements, rate limits, pagination, error conditions, or what happens if no groups exist. The description adds some context but leaves significant gaps.

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 efficiently structured with three sentences and a parameter section. Each sentence adds value: the core purpose, the clustering concept, and the efficiency benefit. The parameter explanations are brief but clear. No wasted words or redundancy.

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 (which handles return values), no annotations, and moderate complexity, the description does well. It covers purpose, usage context, and parameter semantics. However, for a tool with no annotations, it could better address behavioral aspects like authentication, error handling, and performance characteristics to be fully complete.

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 provides clear semantic meaning for all three parameters: course_id and question_id identify the specific question, and output_format specifies the response structure. The description explains what these parameters do beyond their schema titles, though it doesn't provide format details or examples for the IDs.

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 specific action ('List all answer groups for a question') and resource ('AI-Assisted Grading'), distinguishing it from siblings like tool_get_answer_group_detail (which likely shows details of one group) or tool_grade_answer_group (which applies grades). It explicitly mentions clustering of similar student answers and the efficiency benefit of grading by group.

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 explicit guidance on when to use this tool: for listing answer groups to enable efficient batch grading. It distinguishes from 1-by-1 grading alternatives and implies usage in AI-assisted grading workflows. The context of 'Grade one group to grade all members at once' clearly indicates its purpose versus individual grading tools.

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