Crypto market data

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

No annotations are provided, so the description carries the full disclosure burden. It reveals nothing about auth requirements, rate limits, response format, pagination, or freshness/latency of the market data, which matters for live crypto metrics.

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?

A single dense sentence with no filler; each clause maps to a dataset branch. The enumeration ordering matches the schema enum order, but the sentence is not front-loaded with a clear verb+resource opener.

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?

Covers what each dataset is, but for a tool with no annotations and no output schema, it leaves the agent without return-shape expectations or scoping rules (e.g., whether slug is required/allowed for liquidations). Adequate but with clear 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?

Schema description coverage is only 50% and the critical 'dataset' enum has no schema-level description at all. The description compensates by mapping each dataset value to its subject, and clarifies slug semantics per dataset (symbol slug for open-interest, BTC/ETH for options, coin for whales), which materially reduces ambiguity.

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 enumerates the four concrete capabilities (open interest history, liquidations, Deribit options metrics, Hyperliquid whale positions), which is far more specific than the title and distinguishes it from siblings like funding_rates or bitcoin_treasuries. It is a bare list without an explicit verb framing ('returns'/'fetches'), keeping it 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?

Naming each dataset against its subject matter implicitly tells the agent which dataset value to pick for a given question, which is real guidance for a multi-dataset tool. However, there is no explicit when-to-use/when-not statement, no mention of alternatives among siblings, and no prerequisite information.

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