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

With no annotations, the description must carry the behavioral disclosure burden. It does disclose that every response carries source license and attribution, and that the data covers specified frequencies, but it does not describe pagination, default limits, error handling, or whether the output is a time series or cross-sectional. This leaves significant behavioral aspects to inference.

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 long sentence listing many series families; it is information-dense but not particularly concise. Each item adds context about the tool's coverage, yet the length may make it harder to parse quickly. It is front-loaded with the frequency phrase but lacks clear structural breaks.

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?

Given the absence of an output schema and annotations, the description should clarify what the tool returns and how parameters affect results. It covers data scope and mention of licensing, but does not explain output shape, time series behavior, or how `latest`/`geo` influence results. This is a moderate gap for a tool with 5 parameters.

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 already documents all 5 parameters with 100% coverage, so the baseline is 3. The description adds value by providing concrete examples for series_id (e.g., UST.YC.10Y, OECD.IRLT.10Y) and giving context for geographic scope (e.g., '~38 economies', '~190 countries'), which enriches understanding of the geo and series_id parameters beyond their schema descriptions.

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 identifies the tool's scope by enumerating specific high-frequency series families (UST, SOFR, CORRA, CPI, unemployment, etc.) and even gives example series IDs. This distinguishes it from sibling tools like list_series or get_historical_data, though it lacks an explicit verb such as 'retrieves' or 'returns'.

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 when to use this tool – when you need the listed high-frequency data – and instructs users to 'call list_series to browse' as an alternative for discovering series. However, it does not explicitly state when not to use this tool or compare it with get_historical_data or compare_countries, so guidance is partial.

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