list_all_diseases

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

The description discloses that the result is 'a sorted list of disease names' and that diseases are 'unique' and 'present in the dataset,' adding behavioral detail beyond the tool's name. With no annotations, this gives the agent useful expectations about output.

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 compact and well-structured: a clear purpose sentence, a usage hint, and a return format line. Every sentence earns its place with no 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?

For a parameterless, simple listing tool, the description is complete: it explains what it lists, why it's useful, and what the return value looks like. There are no missing details that would prevent an agent from correctly using it.

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 tool has zero parameters and the schema is empty (100% coverage), so there is nothing to document. The description naturally adds no param semantics, but the baseline of 4 applies because no parameter information is needed.

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 function: 'List all unique diseases present in the patient dataset.' This specific verb and resource distinguish it from siblings like find_patients_by_disease, which queries patients, not diseases.

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 phrase 'Useful for discovering what diseases can be queried' provides clear context for when to use the tool. It does not explicitly name alternatives or exclusions, but the guidance is sufficient for a simple discovery tool.

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