treasury_avg_interest_ratesAverage interest rates on US Treasury securities by security type (bills, notes, bonds, TIPS, total marketable / non-marketable) from the Treasury Fiscal Data avg_interest_rates dataset. Returns recent monthly records.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of records to return (default 12) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "limit": 12
+ },
+ {
+ "limit": 24
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, providing strong safety and idempotency guarantees. The description adds that it returns 'recent monthly records,' which aligns with annotations but doesn't add substantial new behavioral info beyond the structured fields.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences efficiently convey the tool's purpose, data source, and output characteristics. No unnecessary words, and key information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one optional parameter and strong annotations, the description is largely complete. It specifies the dataset and periodicity. While it lacks output field details, the tool's simplicity and self-explanatory nature mitigate this gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 100% coverage for the single parameter 'limit' with a clear description. The description adds value by indicating records are monthly, so the limit parameter logically refers to number of months, enhancing understanding beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool provides average interest rates on US Treasury securities by specific types (bills, notes, bonds, TIPS, total marketable/non-marketable) from a named dataset. It distinguishes itself from other treasury tools (e.g., treasury_debt, treasury_receipts) by focusing on interest rates and specifying the source.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when average interest rates are needed and mentions 'recent monthly records,' giving context. However, it does not explicitly state when not to use it or mention alternative tools for related data, but the purpose is clear enough for basic guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most of the surface is dominated by overlapping meta-tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) plus paired helpers (discover_tools/suggest_questions, ai_visibility_check/scan_competitor_ai_presence), so an agent can easily pick the wrong one. The seven treasury_* tools are clearly distinct, but they are a small island in a much larger ambiguous set.
Names are broadly snake_case and family-prefixed (treasury_*, polymarket_*, pipeworx_*), which helps, but the pattern is not consistently verb_noun: ask_pipeworx, bet_research, entity_profile, deep_research, search_within, and recent_changes mix verb, noun, and product-specific naming styles. Within families it is readable, but across the whole set it is inconsistent.
37 tools is too many for a server whose label is 'Treasury Fiscal'; only about six tools are treasury-specific and the rest are unrelated research, betting, memory, and subscription utilities. This is well into the 25+ heavy range and would make tool selection expensive and error-prone.
For a Treasury/Fiscal server the surface is thin: debt, receipts, customs duty, average rates, exchange rates, and net cost cover only a slice of Treasury data. Missing obvious components such as daily yield curves, auction calendars/results, federal outlays/spending by agency or function, and tax or appropriations data; the broad ask_pipeworx router softens but does not fill these as first-class tools.