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

With no annotations provided, the description carries the full burden of behavioral disclosure. It goes beyond basic operation to reveal failure modes ('Every source can fail independently'), data-quality caveats ('history span, candle count, missing days... delisted pairs are flagged'), the mechanical A/B/C grade rule that travels in the payload, and an interpretive caution ('the median altcoin loses against Bitcoin, so a positive number is a description, not an edge'). This is exemplary transparency.

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 a single, very dense paragraph with a long enumeration of metrics, conditions, and caveats. It lacks visual structure (bullets, sections) and is considerably longer than needed for an agent to parse quickly. While every sentence carries useful information, the lack of organization and sheer length hinder readability.

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 tool's high complexity and the absence of an output schema, the description covers the major return components comprehensively (returns, trend, ATH, volatility, liquidity, tokenomics, derivatives, data quality, sources used/unavailable). It does not specify the exact response format, but it still gives an agent enough context to predict what the tool will return and how to interpret 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?

Schema description coverage is 100%, so the schema already documents both parameters. The description adds no new semantic detail beyond what the schema provides (pair case-insensitivity, any Binance USDT pair, detail full adds raw returns). While it reinforces the pair scope, it doesn't add meaning beyond the schema, so the baseline of 3 is appropriate.

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 answers 'Where does this coin stand?' and enumerates a specific set of metrics (returns, relative strength, trend, drawdown, volatility, liquidity, tokenomics, data quality), making the resource and scope clear. It also explicitly differentiates from arena_get_signal_context ('For the strategy-side question...'), which distinguishes it from the closest sibling.

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 states when to use the tool (descriptive snapshot for any Binance USDT pair) and when not to use it ('Descriptive, no signal', and 'For the strategy-side question... use arena_get_signal_context'). It also identifies an alternative tool by name, which is exactly what high-quality usage guidance should do.

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