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Glama

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

by net.agentfund

US Treasury Yield Curve

treasury_yield_curve
Read-onlyIdempotent

Current and recent U.S. Treasury par yield curve rates, with the spreads traders actually watch already computed.

Returns every published tenor (1 month through 30 years) for the latest business day, plus the 2s10s spread, the 3m10y spread, and an inversion flag. Source is the U.S. Treasury's official daily par yield curve (public domain, no attribution required).

When to use: you need risk-free rates for discounting, a read on the curve's shape, or recession-signal context (curve inversion).

When NOT to use: you need intraday quotes (this publishes once per business day) or non-U.S. sovereign curves.

Args:

  • days (integer, optional, default 1): how many recent business days to return, newest first (1-30).

Returns structuredContent: { "asOf": "2026-08-14", "latest": { "date": "2026-08-14", "tenors": { "1M": 3.79, "3M": 3.86, "2Y": 4.17, "10Y": 4.68, "30Y": 5.25 }, "spread2s10s": 0.51, "spread3m10y": 0.82, "inverted": false }, "history": [ ...same shape, newest first... ], "source": "https://home.treasury.gov/..." }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoHow many recent business days of the curve to return, newest first. Default 1.

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Beyond annotations (read-only, etc.), the description discloses update frequency (once per business day), data source, and full output structure. It explains the inversion flag and history ordering, providing rich behavioral context.

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 and front-loaded with the core purpose. The example output is arguably necessary given no output schema, and every section adds value.

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?

With no output schema, the description compensates fully by showing a concrete structuredContent example, covering the latest and history. It also clarifies use cases and limitations, making it complete for a single-parameter tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and the Args section merely repeats the schema's description of 'days' including the 1-30 range and default. No new meaning is added, so baseline 3 is appropriate.

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 first sentence clearly states the tool provides U.S. Treasury par yield curve rates with computed spreads. It distinguishes itself from siblings by specifying the exact data (tenors, 2s10s, 3m10y, inversion flag) and the official source.

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 give concrete context: use for discounting/curve shape/recession signals, not for intraday quotes or non-U.S. curves. This is strong guidance even without naming sibling tools.

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.

Resources