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

With no annotations, the description carries the full burden. It discloses the content returned (source, date, licence, citation) and the purpose, but doesn't specify the format (e.g., text, structured fields) or any side effects. For a read-only metadata tool, this is sufficient transparency, though it could mention that it returns a summary or text block.

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 sentences, no fluff. The first sentence lists exactly what the tool provides, and the second gives the usage context. The most important info is front-loaded, making it efficient for an agent to parse.

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 no parameters and no output schema, the description is complete enough. It tells the agent what it will get and why to use it. The only minor gap is not specifying the return format, but for a provenance tool this is not critical.

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 and the schema is empty with 100% coverage, so there is nothing to explain. Baseline for 0 parameters is 4, and the description adds no parameter details because none exist.

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 provides provenance metadata: source, computation date, licence, and citation for the Runsheetly dataset. This is distinct from sibling tools like dataset_columns (structure) or dataset_stats (aggregates), so an agent can immediately tell it apart.

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 explicitly says 'Read this to attribute a figure correctly,' giving a precise scenario for when to use it. It implies this tool is for citation/attribution purposes, which is a clear use case distinct from data exploration or manipulation.

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