get_dataset

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

Annotations provide safety hints (readOnly/idempotent). The description adds vital behavioral context by disclosing API limitations—which data is intentionally omitted (generation parameters/file path). This prevents an agent from searching for non-existent fields.

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 structure is excellent, front-loading the core functionality and immediate value proposition (what it gets), followed by a crucial cautionary note. It is dense but highly efficient.

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?

Given the absence of an output schema, the description is highly complete. By detailing exactly which information is included and which is intentionally excluded, it removes major ambiguities for the AI agent.

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?

With 100% schema description coverage, the schema adequately defines the `dataset_id`. The description merely reinforces that the ID is the key to fetching comprehensive details, which is typical for a single ID lookup function.

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 is highly specific, using the verb 'Get' and clearly defining the resource (a single dataset or model). It enumerates the specific types of details returned, making the purpose unambiguous to an agent.

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?

While it does not explicitly name alternatives, it provides critical constraints on usage: specifically that generation parameters and on-disk file paths are unavailable. This prevents the agent from making an invalid assumption about the tool's capabilities.

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