get_sample_input

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

No annotations are provided, so the description carries the full behavioral burden. It does disclose the two things returned (an input_data dict and the true diagnosis), but says nothing about determinism of the split, bounds/error behavior for index, or the audit-logging behavior implied by request_id. Adequate but thin for an unannotated tool.

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?

A single front-loaded sentence with no filler; the key output ('ready-to-use input_data dictionary') leads and the secondary return ('true diagnosis') follows. Nothing is wasted.

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?

An output schema exists, so return-value explanation is not required, and the schema fully documents parameters. The description is complete enough for a simple fetch tool, with only a minor gap around how index/test_size/random_state jointly determine the returned row.

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 100%, so all four parameters are already documented in the schema, setting the baseline at 3. The description adds no syntax, format, or interaction details beyond the schema, so it neither compensates nor detracts.

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?

States a specific verb (return) and resource (one test-set patient as an input_data dictionary plus the true diagnosis), and names its audience explicitly ('for the predict tools'), which cleanly separates it from the predict_* siblings that consume that dictionary.

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 implies usage by tying the output to 'the predict tools', so an agent can infer this is a setup/example-fetching step before prediction. However, it gives no explicit when-to-use statement, no guidance on choosing index/test_size/random_state consistently with the split, and no exclusions.

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