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

No annotations are provided, so the description carries the full disclosure burden. It does tell the agent what it will receive back (source, date, licence, citation), which is the main behavioral trait of a zero-parameter read tool, but it says nothing about the response shape or format, and there is no output schema to fill that gap.

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?

Two short sentences, the content list front-loaded and the usage cue last. Every clause earns its place with no filler.

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 parameterless metadata lookup with no annotations and no output schema, the description covers what the tool is and what it returns, which is essentially the whole contract. Only the return format/pagination-style details are left unstated, and those are minor here.

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 takes zero parameters, so per the rubric this defaults to a baseline of 4. There is nothing for the description to clarify beyond the empty schema.

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 names the exact resource (provenance of the Strength Standards Calc dataset) and enumerates the fields returned: source, computation date, licence, citation. It is a noun phrase rather than a verb-led statement, but the scope is unambiguous and cannot be confused with siblings such as dataset_columns or dataset_stats.

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?

'Read this to attribute a figure correctly' gives a concrete trigger for calling the tool. It does not name alternatives or state when not to use it, but for a narrowly scoped metadata tool with obvious siblings, the context is sufficient.

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