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Bea Gov

by io.github.pipeworx-io
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Get Data

get_data
Read-onlyIdempotent

Fetch actual BEA economic data from the specified dataset (e.g. NIPA) using dataset-specific parameters; returns time series values such as GDP, PCE, regional income, and GDP by industry — including the agriculture share of GDP / farm-sector value added as a percent of the economy (GDPbyIndustry dataset).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
datasetYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
BEAAPINoBEA API response wrapper

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / examples
      Previous value: -[
-  {
-    "SeriesID": "A191RA1Q225SBEA",
-    "TableID": "T10101",
-    "dataset": "NIPA",
-    "frequency": "Q"
-  }
-]New value: +[
+  {
+    "dataset": "NIPA"
+  }
+]
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, so the description adds value by specifying the scope of data (NIPA, GDPbyIndustry) and the type of returned values (time series). No contradiction with annotations.

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 a single sentence that front-loads the action and packs in relevant detail (datasets, data types, specific example) without any wasted words.

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?

An output schema exists, so return value structure is covered. The description explains the kinds of data available and the dataset-specific nature, making it sufficient for tool selection. It does not cover pagination or error handling, but these are not essential given the annotations and output schema.

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 schema description coverage is 0% for the single 'dataset' parameter. The description offers two examples (NIPA, GDPbyIndustry) and hints at dataset-specific parameters, but does not provide a comprehensive list or format details, leaving some ambiguity.

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 action ('Fetch actual BEA economic data') and the resource (specified dataset), with concrete examples like NIPA and GDPbyIndustry. It distinguishes from sibling tools such as dataset_list and parameter_list by focusing on data retrieval rather than metadata listing.

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 gives clear context on when to use this tool: when actual BEA time series data (GDP, PCE, etc.) is needed. It implies the tool is for data queries but does not explicitly name alternatives or exclusions, so it falls short of a 5.

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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