fred-mcp-server

geofred_regional_data

Retrieve cross-sectional regional economic data by series group and region type, with options for date, seasonal adjustment, units, and transformations.

Instructions

Get cross-sectional regional data by series group.

Args: series_group: Series group ID (get from geofred_series_group). region_type: Geographic region type. date: Observation date (YYYY-MM-DD). season: Seasonal adjustment — "SA" (adjusted), "NSA" (not adjusted), "SSA" (smoothed adjusted), "SAAR"/"NSAAR" (annualized rates). units: Unit description string (e.g., "Dollars", "Percent"). start_date: Start of date range (YYYY-MM-DD). frequency: Aggregation frequency. transformation: Data transformation (same codes as series observations units). aggregation_method: How to aggregate — "avg", "sum", "eop".

Returns: dict with keys 'meta' and 'data'.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "series_group": "<series_group>",
      "region_type": "<region_type>",
      "date": "<date>",
      "season": "<season>",
      "units": "<units>"
    },
    "name": "geofred_regional_data"
  }
}

Input Schema

Name	Required	Description	Default
`series_group`	Yes
`region_type`	Yes
`date`	Yes
`season`	Yes
`units`	Yes
`start_date`	No
`frequency`	No
`transformation`	No
`aggregation_method`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.2May 29, 2026

TDQS

B3.4/5.0

Behavior2/5

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

No annotations are provided, and the description does not disclose behavioral traits such as side effects, authentication needs, rate limits, or pagination. It only states the return structure (keys 'meta' and 'data'), which is minimal beyond functionality.

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

Conciseness4/5

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

The description uses a structured Args/Returns format with concise parameter lines. It is not verbose and each sentence serves a purpose, though it could be more front-loaded with the primary verb.

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

Completeness3/5

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

Given the tool's complexity (9 parameters, 5 required), the description is adequate but lacks details on error conditions, data limits, or what 'meta' contains. It references obtaining series_group, which is helpful, but overall completeness is moderate.

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?

The description adds value beyond the input schema by providing examples for season ('SA', 'NSA'), units ('Dollars'), and aggregation_method ('avg', 'sum', 'eop'), and specifying date format (YYYY-MM-DD). However, some parameters like region_type and frequency lack examples, and transformation references another tool's codes without elaboration.

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 states 'Get cross-sectional regional data by series group.' This clearly identifies the tool's verb (Get), resource (cross-sectional regional data), and differentiates it from siblings like geofred_series_data (time series) and geofred_shapes (geographic shapes).

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 mentions that series_group can be obtained from geofred_series_group, providing a prerequisite. However, it does not specify when to use this tool versus alternatives like geofred_series_data or provide exclusions for certain inputs.

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