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Glama

US Economic, SEC EDGAR & On-Chain Data (x402)

by net.agentfund

US Real GDP Growth

macro_gdp
Read-onlyIdempotent

Latest U.S. real GDP growth rate, from BEA's National Income and Product Accounts.

Returns the annualized quarter-over-quarter growth rate for the most recent quarter (the headline "how is the economy growing" number), plus the prior two quarters for trend context. BEA publishes this table as a percent-change series already, so no growth-rate math is needed here.

When to use: reading the pace of economic growth, recession-risk context (two consecutive negative quarters), or macro backdrop for a market decision.

When NOT to use: you need GDP in dollar levels, expenditure-component detail (consumption, investment, government, net exports), or real-time/nowcast estimates (this is BEA's official, lagged release).

Args: none.

Returns structuredContent: { "asOf": "2026Q2", "growthAnnualizedPercent": 1.5, "priorQuarters": [ { "quarter": "2026Q1", "growthAnnualizedPercent": 2.1 }, { "quarter": "2025Q4", "growthAnnualizedPercent": 0.5 } ], "source": "https://www.bea.gov/data/gdp/gross-domestic-product" }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds valuable context: it is BEA's official lagged percent-change series, requires no math, and includes a source URL. No contradictions 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?

Well-structured into paragraphs for purpose, usage, exclusions, and return value. Each sentence earns its place, and the example return object is compact yet informative. Nothing is redundant.

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?

Despite no output schema, the description provides a full structuredContent example with all field names and sample values, plus source attribution. For a zero-parameter read tool, this is complete and self-sufficient.

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?

The tool has zero parameters and the description confirms 'Args: none.' With no parameters to document, the baseline of 4 applies, and the description appropriately notes the absence of arguments.

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 returns the latest U.S. real GDP growth rate as annualized quarter-over-quarter, plus prior quarters for trend context. It distinguishes itself from sibling macro tools by explicitly excluding dollar levels and expenditure-component details.

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

Usage Guidelines5/5

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

Provides explicit 'When to use' and 'When NOT to use' sections with concrete use cases (growth pace, recession-risk context) and exclusions (dollar levels, components, nowcasts). This gives the agent clear decision guidance.

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

All US Economic, SEC EDGAR & On-Chain Data (x402) toolsInspect
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TDQS

A4.5/5.0
Disambiguation5/5

Every tool targets a distinct resource and action. The macro_* tools each cover one economic indicator, the edgar_* tools cover different SEC filing types, and the onchain_* tools are split by chain scope (single vs multi), asset type, and operation. Even the two data-cleaning tools are clearly distinct (JSON repair vs table parsing). No two tools appear to do the same thing.

Naming Consistency4/5

Names follow a mostly consistent snake_case pattern with domain prefixes: macro_*, edgar_*, onchain_*. The exceptions are bls_cpi (could be macro_cpi) and the utility tools structured_json_repair and tabular_to_json, which break the prefix pattern but are still descriptive and predictable. Overall, the convention is clear with minor deviations.

Tool Count3/5

21 tools is in the 'heavy' range (16-25). However, the server spans three distinct domains (US economic data, SEC EDGAR, on-chain data), and each tool serves a unique purpose within its domain. While it feels dense, the breadth is justified by the server's stated multi-domain scope.

Completeness4/5

The tool surface covers the major needs in each domain: key macro indicators, common EDGAR filings and searches, and core on-chain reads. Minor gaps exist (e.g., no PPI, no historical on-chain balances, no company CIK lookup), but agents can work around these with existing tools or by combining them.

Resources