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

No annotations are provided, so the description carries full burden. It only lists the data returned and does not mention side effects, authentication needs, rate limits, or whether it is read-only. This lack of behavioral disclosure leaves uncertainty about the tool's impact.

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 two sentences, concise and to the point. Every word adds informational value without fluff or repetition.

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?

The description fully explains the tool's purpose and the type of information returned. Since there is no output schema, no return-value explanation is needed. An agent has enough context to decide when to invoke it.

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 (100% coverage). The baseline for high schema coverage is 3, and the description adds no parameter-specific detail because there are none to describe.

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 returns provenance information (source, computed date, licence, citation) for the Tieoutly dataset. It lacks an explicit verb like 'retrieve' or 'get', but the intent is clear. It is distinct from sibling tools that handle columns, rows, stats, etc.

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 a clear use case: 'Read this to attribute a figure correctly.' This tells an agent when to use the tool. It does not explicitly contrast with sibling tools, but the attribution context is sufficient for basic guidance.

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