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

Given zero annotations, the description carries the full burden and excels by disclosing the internal logic of the data, such as how 'value' and 'currency' re-values are calculated and the specific return structure including pagination (`has_more`, `next_cursor`).

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 tool uses a very dense, single paragraph structure. While the information is comprehensive and efficient, the lack of separation or formatting makes it challenging for an AI agent to quickly parse the definition vs. the return structure.

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?

Due to the complexity of the data and the various optional filters (date ranges, asset types, simulation), the description is highly complete. It covers the data structure, the calculation logic, the ordering, and the pagination mechanism.

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?

The schema coverage is 58%. The description adds semantic value by defining the meaning of the output fields, such as 'contract_value' and 'on-chain proof,' helping the agent understand the data the parameters filter for, but it doesn't dramatically exceed the input schema details.

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 goes beyond merely listing records; it defines them as an 'immutable row per movement of value' and specifies exactly what financial data points (e.g., value, value_usd, tax, tx_hash) constitute the record, clearly distinguishing it from simple payment logs.

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 implies usage by detailing the specific transaction types it tracks (payments, fees, refunds). It also provides a specific instruction on how to handle test data via the 'include_simulated' parameter, which is a practical usage guideline.

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