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Social Bea National Accounts

social_bea_national_accounts
Read-only

US national accounts from the Bureau of Economic Analysis (official API): any NIPA table (e.g. T10101 real GDP percent change, T10105 GDP in dollars, T20100 personal income) at annual, quarterly or monthly frequency for chosen years, returned as BEA publishes the rows (line, description, period, value, unit). Requires the operator's free BEA API UserID. Price: $0.002 in USDC per successful call, paid with x402 (Base, Solana or Arc); failed calls are not charged.

Input Schema

NameRequiredDescriptionDefault
tableYesNIPA table name, e.g. T10101
yearsNoComma-separated years or X for all, e.g. 2025,2026
frequencyNoA (annual), Q (quarterly) or M (monthly)

Output Schema

NameRequiredDescriptionDefault
dataNo
notesNo
tableNo
yearsNo
sourceNo
checkedAtNo
frequencyNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Beyond the readOnlyHint/openWorldHint annotations, it discloses the required free BEA API UserID, the $0.002 USDC charge per successful call, supported payment networks, and that failed calls are not charged. It also describes the returned row format: line, description, period, value, and unit.

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 the core purpose and then layers in prerequisites, output shape, and pricing. It is dense and nearly every phrase adds useful operational context, though the first sentence is long and carries several distinct pieces of information.

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?

For a read-only external data tool with high schema coverage and an output schema, the description covers the missing operational context: official source, table scope, frequency/year handling, auth requirement, billing behavior, and return format. An agent has enough information to invoke it correctly.

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 schema already documents all three parameters. The description reinforces parameter meaning with NIPA table examples and frequency/year usage, but does not add syntax or constraints beyond what the schema provides. Baseline 3 is appropriate.

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 a specific resource and scope: US national accounts from the official BEA API, covering any NIPA table at annual, quarterly, or monthly frequency. It includes concrete table examples such as T10101 and T20100, so an agent can distinguish this from unrelated social/ecommerce siblings without opening the schema.

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?

It gives clear usage context: choose a NIPA table, select years or X for all, and specify A/Q/M frequency. It also states the prerequisite BEA API UserID and the x402 payment model. It does not name alternatives or when-not-to-use conditions, but there is no obvious direct sibling to route away from.

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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