Get U.S. national debt (daily, to the penny) and average interest rates on Treasury securities from the U.S. Treasury.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of records | |
| series | No | debt (national debt, default) or interest_rates | debt |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It does not disclose rate limits, data freshness, authentication, or any behavioral traits beyond the basic purpose.
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?
Single sentence, efficient, no waste. Every word contributes to the purpose.
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?
No output schema, so return format is unexplained. Description lacks context on data range, limit max, or what interest_rates includes. Adequate but not comprehensive.
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?
Schema coverage is 100% with descriptions for both parameters. The tool description adds minimal extra meaning beyond the schema, so baseline 3 is appropriate.
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?
The description clearly specifies the verb 'Get' and the resource 'U.S. national debt' and 'average interest rates on Treasury securities'. It distinguishes from sibling tools that cover other economic data.
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 offers no guidance on when to use this tool versus sibling tools, nor does it explain when to choose 'debt' vs 'interest_rates' for the series parameter.
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.
Each tool targets a distinct economic domain (e.g., demographics, inflation, jobs) with no overlap. The descriptions clearly differentiate the data sources and use cases.
All tools follow a consistent `get_<topic>` pattern using snake_case, making the naming predictable and easy to navigate.
With 7 tools, the server provides a focused yet comprehensive set of macroeconomic indicators without being overwhelming. Each tool serves a clear purpose.
Covers major macroeconomic areas (employment, inflation, GDP, demographics, energy, treasury) but lacks minor categories like trade or consumer confidence. Gaps are slight.