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get_series_data

Read-onlyIdempotent

Fetch regional values for a FRED series across regions, optionally for a specific date or from a start date. Returns dated per-region observations with metadata.

Instructions

GeoFRED / Maps: fetch one regional series' values across regions, optionally over time. Give a regional series_id; with no date, FRED returns the most recent. Set date for a single date, or start_date for every date from then on. The result nests everything under a top-level meta object (FRED's own envelope, mirrored faithfully): the dated per-region values live under meta.data, which maps each observation date to a list of {region, code, value, series_id}; alongside them sit the display labels meta.title, meta.units, meta.region, meta.seasonality, and meta.frequency (see the output schema for the full shape).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNoReport a single date, `YYYY-MM-DD` (default: the most recent).
series_idYesA regional FRED series id, e.g. `SMU56000000500000001`.
start_dateNoReport every date from this one onward, `YYYY-MM-DD`.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
metaYesThe `meta` payload: the descriptive header plus the dated regional values.
Behavior5/5

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

Beyond the annotations (readOnly, idempotent), the description details the return structure (meta object, data layout), default behavior (most recent date), and notes that results are mirrored faithfully. This adds significant context.

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 well-organized: purpose first, then parameter usage, then output structure. Every sentence provides essential information without redundancy.

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

Completeness5/5

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

Given the output schema exists (referenced), the description covers the return format, default behavior, parameter semantics, and safety profile via annotations. It is fully adequate for a data-fetching tool.

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

Parameters4/5

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

Schema coverage is 100%, but the description adds value by explaining the interaction of date and start_date, and clarifies that no date returns the most recent. It also describes the series_id format with an example.

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 starts with a specific verb ('fetch') and resource ('regional series values across regions'), clearly distinguishing it from sibling tools like get_series which retrieve metadata or search tags.

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

Usage Guidelines4/5

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

The description explains when to use each parameter (most recent, single date, date range) and gives an example series_id. It does not explicitly state alternatives but the context of regional maps makes it clear.

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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