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US Treasury interest rates: average rates on bills, notes, bonds and all federal debt, monthly

econ_treasury_rates
Read-only

Average interest rates on US Treasury securities by month from the Treasury's Fiscal Data: bills, notes, bonds, TIPS, floating rate notes and the non-marketable series, with totals for marketable, non-marketable and all interest-bearing debt, for the latest month and up to 24 months. US federal data, public domain, from a dated snapshot. Use for the US government's borrowing cost, a rate trend or macro context; not a live yield curve. Example: months=12. Price $0.01/call via x402 USDC or a card key (/credits), first 25 free; failed calls free. Reads or computes only and changes nothing; each call is charged, so repeats are not free.

Input Schema

NameRequiredDescriptionDefault
monthsNoMonths of history for the totals, 1-24 (default 12)
securityNoOptional filter on the security name: bills, notes, bonds, TIPS, floating, marketable…

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and destructiveHint=false, so the 'reads or computes only and changes nothing' clause is largely redundant. What the description adds beyond annotations is valuable: per-call pricing via x402 USDC or card key, failed calls free, first 25 free, and a 'dated snapshot' rather than live data — all reinforcing the idempotentHint=false that repeat calls cost money.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Coverage and scope are front-loaded, followed by use guidance and then the example. It is a dense single block with a slightly redundant safety clause, but no sentence is filler.

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?

No output schema exists, yet the description specifies what the response contains (per-security averages plus marketable, non-marketable and all interest-bearing totals) and the time horizon. That is sufficient for correct invocation of a two-parameter read 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 schema already states months is 1-24 with default 12, so the 'up to 24 months' text and months=12 example mostly restate it. The description does add a little on the security filter by naming values the schema truncates ('non-marketable series', 'floating rate notes', 'all interest-bearing debt'), but no matching syntax is given.

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?

Uses a specific verb-plus-resource framing: 'Average interest rates on US Treasury securities by month', then enumerates the security classes covered (bills, notes, bonds, TIPS, FRNs, non-marketable) and the aggregates. This clearly differentiates it from siblings like econ_national_debt or econ_inflation without the agent needing to open a schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

'Use for the US government's borrowing cost, a rate trend or macro context; not a live yield curve' gives both a positive use case and an explicit exclusion. It stops short of naming a specific alternative tool for current yield-curve data, so routing to a sibling is still left to inference.

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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