get_sample_image

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

The annotations already establish that the operation is read-only and idempotent. The description adds meaningful context by disclosing the output format (PNG image) and the scope (a single sample from the suspect dataset), though it does not cover error behavior, image resolution, or access requirements.

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 a single, front-loaded sentence with no wasted words. It places the verb and returned resource first, followed by the intended use case.

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 read-only image fetch, the description plus annotations cover the basic purpose and output type. However, the lack of parameter semantics and edge-case behavior leaves gaps in fully equipping an agent to invoke it correctly in all cases.

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 for the two undocumented parameters. It implies that experiment_id selects an experiment and sample_index selects a sample, but it does not explicitly define either parameter, explain the zero-based indexing, or clarify that sample_index indexes the suspect dataset.

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, 'Fetch', and clearly identifies the resource: a single sample from an experiment's suspect dataset, returned as a PNG image. It is clear what the tool does, but it does not explicitly distinguish itself from nearby dataset-oriented siblings such as get_dataset or get_sanitized_dataset.

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 'for visually inspecting a flagged sample' gives useful implied usage context. However, it does not state when to use this tool versus alternatives, nor does it provide exclusions or preconditions.

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