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

Annotations already establish readOnly, idempotent, openWorld false, and non-destructive behavior. The description adds value beyond these by explaining that the tool contains no live market data and that its findings include measured sample/verdict pairs plus clearly marked unmeasured conventional readings. This gives the agent an accurate model of what the tool will and will not provide.

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 and front-loads the core nature of the tool before adding usage guidance. It is dense but not wasteful; nearly every clause adds a distinct piece of information. The 'part worth calling for' aside is slightly editorial but does not undermine clarity.

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?

Given the simple one-parameter shape and existing annotations, the description covers the tool's content, its non-live nature, and when to call it. However, with no output schema and no per-topic explanation, the agent is left to infer what each enum value will produce and what the returned 'verdict' or 'sample' structure looks like. Adequate, but with meaningful gaps.

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?

There is one required enum parameter with 0% schema description coverage, so the description must compensate. It mentions 'topic' once but never explains the eleven enum values or how choosing one affects the returned knowledge. For example, an agent cannot tell from the description what 'workflow' or 'whale-positioning' would return. The enum names are somewhat self-descriptive, but the description itself does not provide the needed semantics.

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 identifies a knowledge/domain-guide resource for crypto analysis and lists concrete content: topic constraints, the Basis tool chain, measured findings with sample and verdict, and unmeasured conventional readings marked as such. It does not use an explicit retrieval verb and does not name a sibling, but the 'no live market data' boundary distinguishes it from market-data tools in the sibling list.

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 gives explicit call contexts: 'Call it before analysing a chart pattern, interpreting an indicator level, designing a strategy or judging a result that looks strong.' It also states a clear limitation ('It carries no live market data'), though it does not name specific alternative tools or say when not to use it beyond that live-data exclusion.

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