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

With no annotations, the description carries the transparency burden. It discloses that the data is derived from a 'committed data-status census,' that stale series are reported as stale, and it enumerates the honest state values (current | due | stale | unknown). This conveys read-only, non-live behavior without relying on annotations.

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, each earning its place: the first defines the output contract, the second gives the decision-use case, and the third explains the data source and stale handling. It is front-loaded with the core purpose and contains no filler.

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?

For a single-optional-parameter tool with no output schema, the description covers the return concept well: fields per series, possible state values, and the derived source. It could go slightly deeper on how 'due' vs 'stale' is determined, but it is complete enough for correct 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?

The input schema fully documents the single optional parameter (series key, slug, title, or alias; omit for all series), so the description needs to add little. It does not add new parameter syntax or format details beyond the schema, landing at the baseline for full schema coverage.

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 uses a specific verb-resource pair ('Get the freshness contract for MFG Calcs' live data') and enumerates exactly what the result contains: cadence, latest period, age in days, and state. This scope is distinct enough from siblings like get_change_events or get_vintages that an agent can identify the tool's job without opening any schema.

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 gives clear context for when to call it: it 'decides answer from memory or call the tool' and is 'the cheapest way to know whether a re-sync is worth it.' It does not explicitly name alternatives or state when not to use it, but the decision trigger is explicit and actionable.

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