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

The description adds behavior beyond the annotations: it compares series side-by-side over the same date range and returns observations for each series. With readOnlyHint and idempotentHint already covering safety, this extra detail about output shape is valuable.

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?

Three sentences with no filler: the first states the core action and scope, the second states the output, the third gives practical use cases. Everything earns its place.

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, combined with the fully-covered schema and annotations, gives an agent enough to invoke the tool correctly: required series_ids, optional start/end/limit, output type, and use cases. Minor gaps like non-overlapping date-range behavior are not necessary for selection and invocation.

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 100%, so the schema already documents all four parameters. The description does not add parameter-specific details, which is acceptable at baseline 3.

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 action ('Compare'), the resource ('FRED series'), and the scope (2 to 5 series, same date range). It also specifies what is returned ('observations for each series'), which distinguishes it from single-series FRED tools like fred_observations.

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?

It provides clear use cases ('ratio analysis', 'cross-series correlation') that signal when to choose this tool. It does not explicitly name alternatives or exclusions, but the context is strong enough for an agent to select this over single-series or search-focused FRED tools.

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