Exchange insurance funds: size, 24h and 7d change, fund against open interest, daily history

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

With readOnlyHint=true and openWorldHint=true, the annotation already covers safety. The description adds value by disclosing data provenance and limitations: 'A fund is a balance the venue reports, not an audit of its reserves' and the nuance about OKX's own published total vs. sum of priced pools elsewhere. These details help an agent reason about trustworthiness and interpretation without contradicting the annotations.

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 dense but every sentence earns its place: usage triggers first, then return details, then parameter semantics, then a caveat. There is no filler or repetition of schema content. The structure front-loads the most important decision information for an agent: when to call.

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?

For a read-only data retrieval tool with no output schema, the description provides a thorough picture of available metrics, per-venue coverage, parameter behavior, and data caveats. An agent can confidently invoke it for the stated use cases. There is no missing critical context such as rate limits or auth requirements, which annotations already cover.

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 coverage is 100%, so the baseline is 3. The description goes beyond the schema by clarifying what 'pools' actually returns: 'every pool row (the contracts it covers, asset, balance, USD)', which adds structural meaning that the schema's terse 'Include every pool row' lacks. The venue and history_days parameters are adequately covered by the schema, and the description reinforces their use with context like 'per covered venue' and 'daily closes per venue.'

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 uses a specific verb+resource pairing: 'Call this when the user asks how big an exchange's insurance fund is...' and enumerates the exact metrics returned (fund USD value, per-asset breakdown, 24h/7d change, percent of open interest, daily closes). This clearly separates it from the many sibling data tools, particularly get_open_interest and get_coverage, by naming the insurance fund domain explicitly. The title reinforces the scope with concrete metric names.

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 opens with explicit when-to-use triggers: asking about fund size, shrinking/used after crash, how much exchanges hold, or comparison to open interest. These are clear, actionable conditions. It does not explicitly name alternatives or state when not to use it, so it stops short of a 5, but the context is strong and unambiguous.

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