create_dataset

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

With no annotations, the description carries the burden of behavioral disclosure. It states 'documentation-only tool,' which clearly indicates the tool does not perform mutations and likely returns instructional content. It does not describe return format, but for a zero-parameter docs tool this is sufficient.

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 extremely concise at two sentences, front-loads the usage context, and contains no filler. Every word 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 documentation tool with no parameters and no output schema, the description is complete enough. It tells the agent exactly when to use it and what to expect, though it could briefly mention what topics the documentation covers.

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, so the schema fully covers everything. The description needs no parameter information, and the baseline for zero-parameter tools is 4.

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 purpose as a documentation resource for understanding dataset creation. It explicitly uses 'documentation-only' to distinguish itself from actual dataset creation tools, aligning with the resource and verb requirements.

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 explicitly says 'Call this tool when you need to understand how to create datasets,' providing clear when-to-use context. However, it does not name specific alternatives or state when not to use it, but the 'documentation-only' hint implies exclusion of actual creation tasks.

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