call_timeseries

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

With no annotations, the description carries the full transparency burden. It explains the data source and return format but does not disclose constraints like rate limits, authentication requirements, or whether the tool is strictly read-only. The behavioral disclosure 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 highly concise: a single sentence for purpose followed by a structured list of arguments. Every sentence adds value, and the format is easy to scan.

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?

The description covers the tool's purpose, all parameters, and the return structure (JSON with total_results and data[]). Given the output schema exists, further detail on return values is not needed. Minor gaps exist regarding error handling and edge cases, but overall it is sufficiently complete for a query tool.

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?

Given 0% schema description coverage, the description compensates well by explaining each parameter in the Args section, including defaults and interaction rules. It could be improved by detailing accepted values for fields and the format for company_id/account_id.

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 clearly states the tool retrieves per-day call volume using a specific API endpoint, with a daily trend line instead of client-side bucketing. It uniquely distinguishes itself from siblings like call_stats and list_calls by specifying the output is an aggregated time series.

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 provides explicit parameter guidance, such as the default for days and the precedence rules between days and start_date. However, it lacks direct comparison to sibling tools like call_stats or compare_periods to help the agent decide when to use this specific tool.

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