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Glama

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

by net.agentfund

US CPI Inflation

bls_cpi
Read-onlyIdempotent

Latest U.S. CPI inflation from the Bureau of Labor Statistics, with the rates already computed.

BLS publishes index levels, not inflation rates. This tool does the arithmetic: headline and core (all items less food and energy) CPI, each with year-over-year and month-over-month percent change. Year-over-year uses not-seasonally-adjusted data and month-over-month uses seasonally adjusted, matching how these figures are conventionally reported.

When to use: you need the current inflation rate, a real-versus-nominal adjustment, or CPI context for a macro decision.

When NOT to use: you need PCE (the Fed's preferred gauge), regional or category-level CPI detail, or a long historical series.

Args: none.

Returns structuredContent: { "asOf": "2026-07", "periodName": "July 2026", "headline": { "index": 333.918, "yoyPercent": 2.9, "momPercent": 0.2 }, "core": { "index": 337.133, "yoyPercent": 3.1, "momPercent": 0.3 }, "source": "https://www.bls.gov/cpi/" }

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 and idempotentHint, and the description adds valuable methodological context: BLS publishes index levels, not rates; the tool computes YoY from NSA data and MoM from SA data. This explains behavior beyond the structured annotations without contradiction.

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 well-organized with clear sections ('When to use', 'When NOT to use', 'Args', 'Returns structuredContent'). Every sentence adds value, and the sample output makes the structure concrete 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 lacking an output schema, the description provides a full structuredContent example with field names and sample values, covering asOf, periodName, headline, and core. It explains the data source and the computation method, making it complete for a simple read-only 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?

The tool has zero parameters, so the description appropriately states 'Args: none.' With no parameters to document, the baseline of 4 is met. The description could have added even more context about parameterlessness, but it's already explicit.

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 tool's verb and resource: 'Latest U.S. CPI inflation from the Bureau of Labor Statistics, with the rates already computed.' It specifies exact outputs (headline and core CPI, YoY and MoM) and naturally distinguishes from siblings like macro_pce by mentioning what it does not cover.

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?

Explicitly includes 'When to use' (need current inflation rate, real-vs-nominal adjustment, CPI context) and 'When NOT to use' (PCE, regional/category-level detail, long historical series). Names the alternative (PCE) and clarifies the tool's scope relative to siblings.

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