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Glama

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

by net.agentfund

US PCE Inflation (Fed's Preferred Gauge)

macro_pce
Read-onlyIdempotent

The Fed's preferred inflation gauge: Personal Consumption Expenditures (PCE) price index, headline and core.

The Federal Reserve targets PCE inflation, not CPI, when setting policy. Returns the headline index and "PCE excluding food and energy" (the actual core measure the Fed watches), each with year-over-year and month-over-month percent change computed from BEA's published index levels.

When to use: Fed-policy reasoning, comparing the Fed's actual inflation target against CPI, macro research that specifically needs PCE rather than CPI.

When NOT to use: you want CPI (use bls_cpi, which is timelier and what headlines usually report) or category-level PCE detail.

Args: none.

Returns structuredContent: { "asOf": "2026-06", "headline": { "index": 129.5, "yoyPercent": 2.6, "momPercent": 0.3 }, "core": { "index": 131.2, "yoyPercent": 2.8, "momPercent": 0.2 }, "source": "https://www.bea.gov/data/personal-consumption-expenditures-price-index" }

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, idempotentHint, and destructiveHint false. The description adds meaningful context: defines core as 'PCE excluding food and energy', explains the Fed's preference for PCE over CPI, and shows the exact return structure in the JSON example. 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?

The description is well-organized with a clear lead sentence, contextual explanation, direction on usage, and a compact JSON example. Every sentence adds value, and the structure front-loads the essential definition.

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 parameterless tool with no output schema, the description is fully sufficient. It covers what is returned (headline and core with indices and percent changes), the source URL, and the exact structuredContent format. Sibling differentiation is also addressed. No gaps remain.

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?

With zero parameters, the baseline is 4. The description explicitly states 'Args: none', confirming no inputs are needed. There is no additional parameter information to provide.

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 identifies the tool as returning PCE price index data (headline and core) with YoY and MoM changes, and explicitly distinguishes it from CPI via the 'When NOT to use' section pointing to bls_cpi. The resource and scope are specific and unambiguous.

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' guidance, naming bls_cpi as the alternative for CPI. This gives clear decision criteria for tool selection.

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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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.

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