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Glama

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

by net.agentfund

US Economic Release Calendar

macro_release_calendar
Read-onlyIdempotent

Upcoming U.S. economic data releases, with dates and times, from the official BLS news-release schedule.

Answers "what macro data drops next, and when" without scraping a web page. Covers the BLS release set that moves markets: CPI, PPI, the Employment Situation (nonfarm payrolls and unemployment), JOLTS, Employment Cost Index, real earnings and productivity.

When to use: planning around data risk, checking whether a print lands before a decision, or building a watchlist of upcoming events.

When NOT to use: you need the released VALUES (use bls_cpi for CPI), Fed/FOMC meeting dates, or non-U.S. statistical calendars.

Args:

  • limit (integer, optional, default 10): maximum releases to return (1-100), soonest first.

  • filter (string, optional): case-insensitive substring match on the release title, e.g. "CPI".

Returns structuredContent: { "asOf": "2026-08-14", "count": 1, "releases": [ { "date": "2026-09-10", "datetime": "2026-09-10T12:30:00Z", "title": "Consumer Price Index", "source": "BLS" } ], "source": "https://www.bls.gov/schedule/" }

Only releases on or after today are returned, soonest first.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of upcoming releases to return, soonest first. Default 10.
filterNoOptional case-insensitive substring filter on the title, e.g. "CPI".

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds behavioral context beyond annotations: it sources from BLS schedule, returns only releases on/after today, sorts sooner-first, and provides an output shape example. Slight gap: no mention of rate limits or potential staleness, but the additional behavior is clear.

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-structured with a clear lead sentence, explicit use cases, parameter details, and a structuredContent example. Each section earns its place; the content is informative without redundancy.

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?

Even without an output schema, the description provides a complete structuredContent example, covers all parameter behavior, explains the when/why, and lists explicit non-use cases. Combined with rich annotations, the tool is fully specified for an agent to select and invoke correctly.

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?

Schema coverage is 100% and both parameters have descriptions. The description adds extra semantics by explaining the default limit (10), the case-insensitive substring behavior for filter, and giving concrete examples like 'CPI'. This exceeds the baseline 3 without being verbose.

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 uses a specific verb ('Upcoming... releases') and resource ('BLS news-release schedule'), clearly stating it provides dates and times for future economic data. It distinguishes from siblings by explicitly noting it does not provide release values (like bls_cpi) or Fed/FOMC dates.

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?

The description has explicit 'When to use' and 'When NOT to use' sections, naming alternatives (bls_cpi for CPI) and exclusions (Fed/FOMC, non-U.S. calendars). This fully guides selection among sibling tools.

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