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Glama

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

by net.agentfund

US Jobs Report

macro_jobs
Read-onlyIdempotent

Latest U.S. labour-market data from the Bureau of Labor Statistics, with the headline changes computed.

Returns the unemployment rate, labour force participation rate, total nonfarm payrolls, the month-over-month change in payrolls (the "jobs added" number that leads the Employment Situation report), average hourly earnings, and year-over-year wage growth. All series are seasonally adjusted.

BLS publishes levels; the month-over-month and year-over-year changes are computed here.

When to use: reading the state of the labour market, wage-inflation context, or Fed-policy reasoning.

When NOT to use: you need state or metro level detail, industry breakdowns, or JOLTS openings and quits.

Args: none.

Returns structuredContent: { "asOf": "2026-07", "periodName": "July 2026", "unemploymentRate": 4.1, "participationRate": 62.4, "nonfarmPayrolls": 158858, "payrollsChange": 73, "avgHourlyEarnings": 37.62, "earningsYoyPercent": 3.8, "source": "https://www.bls.gov/ces/" }

Payrolls are in thousands of jobs, so payrollsChange 73 means +73,000 jobs on the month.

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 indicate read-only, idempotent, non-destructive. The description adds meaningful behavioral context: seasonally adjusted series, computed month-over-month and year-over-year changes from BLS levels, and units explanation for payrolls. The example output clarifies the return shape. 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?

Front-loaded with a one-line purpose, followed by return details, usage guidance, and an example. Every section serves a clear role; no filler or redundancy. The example JSON and units note add practical value without bloat.

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 fully documents return values via a concrete JSON example and explains payroll units. It covers usage context, exclusions, and data source, making it self-sufficient for a no-parameter tool.

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?

Tool has zero parameters, and baseline is 4. The description explicitly states 'Args: none,' which is sufficient. No parameter explanations needed.

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 provides latest U.S. labour-market data from BLS with computed headline changes, and enumerates the specific metrics returned. It distinguishes itself from siblings via the when-NOT-to-use section explicitly naming state/metro detail, industry breakdowns, and JOLTS.

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?

Explicit 'When to use' and 'When NOT to use' sections provide clear context for labour-market assessment against alternatives. It names exclusions (state/metro, industry, JOLTS) and implies alternatives like macro_gdp or bls_cpi for other macro indicators.

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