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

No annotations are provided, so the description carries the burden. It implies a non-mutating read operation with 'Read this', and lists the output content, but it does not describe behavior such as errors, availability, or absence of side effects. For a zero-parameter provenance query, this is acceptable but not fully transparent.

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 efficient sentence that front-loads the content and ends with the practical use case. Every clause earns its place, and there is no redundant repetition of the tool name or title.

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 no-parameter, no-output-schema tool, the description is sufficiently complete: it states what information is provided and why an agent would use it. It could add a bit more about citation format or licensing specifics, but nothing critical is missing for correct invocation.

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 zero parameters, the schema provides no semantics to complement. The description adds meaningful context by enumerating exactly what information will be returned (source, date, licence, citation), which fully compensates for the absence of parameters.

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 what the tool exposes: source, computation date, licence, and citation for the BioBricks dataset. It is distinct from sibling tools like dataset_search or dataset_stats because it focuses on provenance and attribution, though it does not explicitly name a sibling as the alternative.

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 'Read this to attribute a figure correctly' gives an explicit use case for when this tool is appropriate. It does not discuss when not to use it or compare against sibling tools, but the context is clear enough for a simple read-only provenance tool.

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