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

With no annotations, the description carries the full burden and does well: it discloses that no model is involved ('facts, not opinion, so you do the judging'), that only topic-relevant endpoints are fetched, that data age is reported per source, and that it is free with no key. Minor gaps remain around rate limits and failure behavior when a question matches no known topic.

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 leading 'ONE CALL instead of five' claim and the example questions front-load the value, and the return-field enumeration is compact. It is dense but nearly every clause carries information; the parenthetical topic list is the only slightly bulky part.

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?

No output schema exists, and the description fully enumerates the four response fields with their semantics, including that `facts` mirrors `answer`'s numbers as JSON and `sources` carries data staleness. For a two-parameter tool of this complexity, an agent has enough to call it and interpret the result.

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 0%, so the description must compensate. It adds meaningful context for `question` (topics parsed: liquidations, funding, open interest, whales, levels, sentiment; English or Russian), but the `coin` parameter with its BTC default is never explained — it is only incidentally visible in one example question.

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 states a concrete verb and output ('ask in plain words' and get `answer`/`facts`/`sources`/`caveats`) and explicitly frames what the tool does versus a narrative sibling. An agent can distinguish it from explain_market and get_precedents without opening any schema.

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?

It names both alternatives and the exact condition that selects each: get_precedents for the base rate of current state, explain_market for an LLM narrative. The four example questions further show the shape of an appropriate invocation.

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