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

Annotations cover the safety profile (readOnly, idempotent, non-destructive), yet the description adds real behavioral context: results are "every ALFRED vintage on record ... oldest first," consecutive vintages often repeat a value, and the agent should compare `first_release.value` to `latest.value` or scan `revisions`. That pre-empts a common misread. It omits any note on vintage availability limits (ALFRED history depth) or what happens for a date with no vintages.

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 first sentence front-loads what the tool returns, the second gives the use case and examples, and the third warns about duplicate consecutive vintages and how to read the response. No filler, and the ordering matches how an agent decides then invokes.

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?

With an output schema present, an output schema plus annotations, plus 100% schema coverage, the description only needed to cover purpose, applicability, and the interpretation gotcha — and it does all three, even pointing at the specific response fields to compare.

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 both real parameters are already documented in the schema, including the YYYY-MM-DD format, the must-match-an-actual-observation rule, and the GDPC1/PAYEMS examples. The description's only added nuance is stressing that observation_date is a single observation rather than a range, which is marginal over 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?

"How ONE observation (one date) in a FRED series was revised over time, from first release through every later vintage to the latest" names a specific resource (a single series/date observation), the operation (revision history), and the temporal scope (first release → latest vintage). The emphasis on ONE observation separates it cleanly from series-level siblings such as fred_get_series and fred_vintage_dates.

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?

"Use on series that get revised after initial publication (e.g. GDP GDPC1, payrolls PAYEMS)" gives an explicit positive condition with concrete examples, which is enough to pick this tool over a plain series fetch. It stops short of stating when NOT to use it or naming the sibling to use for unrevised data or full-series history.

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