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get_regional_data

Read-onlyIdempotent

Retrieve region-level data for a FRED series group on a specific date. Returns values for each region keyed by date.

Instructions

GeoFRED / Maps: fetch a region cross-section for a series group — the value in every region (state, county, MSA, country, or BEA region) on a given date. All arguments are required: series_group id, region_type, date, units (a free-text measurement label FRED echoes into the title, e.g. Dollars), frequency, and season. Returns the values keyed by date.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateYesThe date to report, `YYYY-MM-DD`.
unitsYesUnit-of-measurement label — free text that FRED echoes into the result title (e.g. `Dollars`), not a transformation code.
seasonYesSeasonal adjustment.
frequencyYesReporting frequency.
region_typeYesRegion granularity to break the data down to.
series_groupYesThe GeoFRED series-group id, e.g. `882`.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
metaYesThe `meta` payload: the descriptive header plus the dated regional values.
Behavior3/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, so the description does not need to restate safety. It adds value by indicating that values are returned keyed by date and that all args are required. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences: the first defines purpose and output, the second lists required arguments. It is front-loaded, efficient, and contains no extraneous information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (6 required parameters), the presence of an output schema, and comprehensive annotations, the description provides sufficient context. It explains the input constraints and return format, though it could briefly mention the output schema or common use cases.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the description cannot add much beyond what the schema provides. It does add context that the 'units' parameter is a free-text label echoed into the title, which is helpful. However, overall the description does not significantly enhance parameter understanding beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('fetch') and the resource ('region cross-section for a series group'). It specifically names the region types (state, county, MSA, country, BEA), distinguishing it from sibling tools like get_series_data or get_category_series which handle time series or category data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description lists all required arguments but does not provide explicit guidance on when to use this tool versus alternatives. While the tool is specialized for regional cross-sections, it could improve by noting that for single series time series, other tools like get_observations or get_series data are more appropriate.

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

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