get_random_text

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

With no annotations, the description must carry behavioral disclosure, and it does state the core behavior: returning a random text segment from the library. However, it does not explain what shape the returned segment takes, how randomness is scoped, or any limits, leaving behavior only lightly specified.

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?

Two short sentences, no filler: the first states the operation and scope, the second states practical uses. It is front-loaded and every sentence earns its place.

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 no-parameter read tool, the description conveys the resource, the random nature, and likely use cases, which is enough for an agent to decide to call it. It is slightly incomplete because there is no output schema and the description does not hint at the return payload structure.

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 zero parameters and 100% coverage, so there is nothing for the description to add about arguments. Baseline 4 for a no-parameter tool 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 uses a specific verb and object: 'Get a random text segment from the Sefaria library,' so an agent can tell this returns unpredictable content rather than a specific ref. It does not explicitly distinguish this from the sibling get_random_by_topic, so it misses the top level of sibling differentiation.

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?

It gives concrete use cases: 'Useful for daily study suggestions or testing,' which is a clear context for invocation. It does not mention when another tool should be chosen instead, so it stops short of a 5.

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