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lzinga

US Government Open Data MCP

by lzinga

bea_gdp_national

Read-onlyIdempotent

Fetch U.S. national GDP from NIPA tables. Access GDP, growth, components, and deflators for quarterly or annual periods.

Instructions

Get U.S. national GDP data from the NIPA tables. Shows GDP, GDP growth, components (consumption, investment, government, net exports), and deflators.

Common table names:

  • T10101: GDP and major components (real)

  • T10106: GDP and major components (nominal)

  • T10111: GDP percent change

  • T20100: Personal income and its disposition

  • T30100: Government receipts and expenditures

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearNoYear(s) to fetch. Use 'X' for all, 'LAST5' for last 5, or specific year. Default: LAST5
frequencyNoFrequency: Q=quarterly (default), A=annual, M=monthly
table_nameNoNIPA table name (default: T10101 — Real GDP). Other: T10106 (nominal GDP), T10111 (% change), T20100 (personal income)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv2026.4.11
    • removedInput schema / additionalProperties
      Removed value: -false
  2. First observedv2026.3.9

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is established. The description adds useful behavioral context by explaining what data is shown and mapping common NIPA table names to their contents. No contradiction with annotations is present.

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 is front-loaded with a clear one-sentence summary followed by a scannable bullet-style table list. It contains no filler, though some table details duplicate the schema descriptions.

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?

For a tool with no required parameters and no output schema, the description plus schema covers the main invocation decisions: table, year, and frequency. It could be slightly more complete by pointing to related BEA tools for state or industry data, but that is not essential for calling this tool correctly.

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 description coverage is 100%, so the baseline is 3. The description adds value beyond the schema by pairing table IDs with readable meanings, including extra detail like T10106 nominal GDP, T10111 percent change, and T30100 government receipts, helping an agent choose table_name more confidently.

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 it 'Get[s] U.S. national GDP data from the NIPA tables' and enumerates the covered series: GDP, GDP growth, components, and deflators. It also lists specific table names, which makes the tool's purpose unambiguous and distinguishes it from state-level and industry-level BEA siblings.

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 makes the intended scope explicit: national GDP and NIPA tables, and gives a table catalog that helps an agent choose table_name. It stops short of explicitly naming alternatives like bea_gdp_by_state or bea_gdp_by_industry, so it provides clear context but no explicit when-not-to-use guidance.

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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