study_list

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

The description specifies the default model is used if not provided, implying a read operation. However, it does not explicitly state that the tool is non-destructive or safe, nor does it mention any side effects. No annotations are present to contradict.

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, with a clear first sentence stating the purpose, followed by structured args and returns. It avoids unnecessary detail while being informative.

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?

For a simple listing tool with one optional parameter and no output schema, the description adequately covers purpose, parameter, and return value. It could mention if a model must be loaded, but is otherwise 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?

The description explains the parameter 'model_name' includes its default value (current model), adding meaning beyond the input schema which only provides the title and type. This clarifies the optional behavior.

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 it lists all studies in a model, with a specific verb (list) and resource (studies in a model). It is distinct from sibling tools like study_solve or model_list.

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?

No guidance is provided on when to use this tool versus alternatives like study_get_progress or when listing studies is appropriate. No exclusions or prerequisites are mentioned.

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