sw_validate

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

With no annotations, the description carries the full burden. It explains that the tool performs three types of checks and returns a 'passed' flag, and instructs the user to fix configuration if 'passed' is false before proceeding. This is useful behavioral context, though it does not disclose side effects or operational constraints.

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 concise, front-loaded with the main purpose, and organized into clear sections (check categories and usage recommendation). Every sentence adds value without unnecessary verbosity.

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 tool with one parameter, no annotations, and an output schema, the description covers the main purpose, usage timing, and key output ('passed'). It does not provide details on dataset_id or prerequisites, but the overall context is reasonably complete.

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 input schema has only one parameter, dataset_id, with no description (0% coverage). The tool description does not explain what dataset_id should contain, how to obtain it, or any format. The only hint is the reference to channel datasets, leaving the parameter semantics largely undocumented.

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 identifies the tool's purpose: validating the trustworthiness of a channel dataset for drawing conclusions. It lists three specific check categories (3GPP 38.901, physical laws, statistics) which adds detail, but it does not explicitly distinguish from sibling tools such as sw_gate or sw_calibrate.

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 provides explicit usage guidance: 'It is recommended to run once before Monte Carlo simulation.' This gives a clear context for when to use the tool. It does not mention alternatives or exclusions, but the recommendation is specific and actionable.

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