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

With no annotations, the description carries the full behavioral burden. It does add scope constraints beyond the schema: 'history' suggests time-series, 'recent' suggests a time window, and options are limited to BTC or ETH. However, it does not disclose side effects, rate limits, authentication needs, or response behavior. For a read-only data tool this is adequate but not comprehensive.

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, well-organized sentence that lists four datasets with their key qualifiers. It is front-loaded with the primary mode (open interest) and avoids fluff. It could lead with a clearer subject-verb construction, but it is compact and informative.

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 multi-mode tool with two parameters and no output schema, the description covers each dataset mode and the relevant slug constraints. It does not explain the output format or clarify whether slug is needed for liquidations, but for invocation purposes the agent can correctly select dataset and provide slug where relevant. The main gaps are behavioral details rather than invocation essentials.

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 50% (slug is described, dataset is not), but the description compensates by explaining what the dataset enum means for each mode and how slug should be used (e.g., 'open-interest: symbol slug', 'options: BTC or ETH', 'whales: coin'). It maps slug requirements per dataset, though it does not explicitly state that liquidations ignore slug.

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 names four distinct crypto datasets (open interest, liquidations, options metrics, whale positions) and clearly states what each delivers. It lacks a single explicit verb like 'retrieves' and the title is generic, but the resource and scope are identifiable. It is distinguishable from sibling tools because it is crypto-specific and names exchanges/venues like Deribit and Hyperliquid.

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?

No explicit when-to-use or alternative-routing guidance is given, but the dataset list implies the tool is for these specific crypto data needs. There is no mention of when not to use it or what to use instead. The context of sibling tools (equities, rates, etc.) makes the crypto scope clear, but the description itself does not state selection criteria.

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