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

No annotations are provided, so the description carries the full burden. It discloses the returned content (source, computed date, licence, citation) and implies a pure read with no inputs and no side effects, which is the key behavioral fact here. It stops short of stating anything about freshness, limits, or reuse terms of the licence itself.

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, with the returned fields front-loaded and the use case second. No filler, no repetition of the title, and nothing that could be cut.

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?

This is a low-complexity, parameterless metadata lookup with no annotations and no output schema, so the description must stand in for the return shape — and it does by enumerating the four fields. It is adequate for correct invocation, with only minor room to say how the citation should be formatted.

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 there is no parameter meaning to convey; the baseline for a parameterless tool applies. Nothing in the description misleads about inputs.

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 resource (the PropFirmPicker dataset's provenance) and enumerates exactly what it yields: source, computation date, licence, and citation. That distinguishes it from siblings like dataset_columns, dataset_stats, and dataset_search, which return data rather than metadata. It lacks a verb, so it reads as a content label rather than a stated action.

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 triggering condition: use it when a figure needs citing or attributing. It does not name alternatives or state when not to use it, but the intended context is clear enough for an agent to select it.

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