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

Annotations already declare readOnlyHint=true and openWorldHint=true, so safety is covered. The description adds above the annotation bar by disclosing the statistical methodology (95% Wilson interval), the confidence-signalling fields (sample_size, low_confidence), and the fact that these are always present, which tells the agent how to interpret reliability.

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 tight sentences with zero filler: the output contract comes first, the call condition second. Every clause earns its place and nothing is buried.

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?

With no parameters and no output schema, the description carries the return-shape burden and does so by naming the three guaranteed fields. It is close to fully self-sufficient, missing only a note on the time window or scope of "past signals."

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 nothing for the description to disambiguate and the baseline is 4. The schema is trivially complete and the description correctly adds no parameter discussion.

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?

States a specific measurement (accuracy of past signals vs later Kraken prices) and enumerates the returned metrics, so an agent knows exactly what the tool yields. It doesn't explicitly distinguish itself from nearby siblings like get_forecast_results or get_history, so it stops short of a 5.

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?

"Call this when you decide how much to trust GeoQuant signals" gives a concrete triggering situation for the tool. It offers no exclusions or named alternatives, but the use case is unambiguous.

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