Get U.S. CPI inflation data from the Bureau of Labor Statistics. Returns latest value, YoY change, and MoM change.
| Name | Required | Description | Default |
|---|---|---|---|
| months | No | Months of history (default 13) | |
| series | No | all_items (default), core (ex food/energy), food, energy, shelter, medical | all_items |
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 bears full burden. It only states the return data (latest, YoY, MoM) but does not disclose potential side effects, authentication needs, rate limits, or whether the data is cached or live. This is minimal behavioral disclosure for a read tool.
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?
The description is two sentences with no unnecessary words. It front-loads the purpose and specifies return values efficiently.
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?
With no output schema, the description provides some context on returns (latest, YoY, MoM) but lacks details on data format (e.g., object structure, units). For a 2-parameter tool, it is moderately complete but could be more explicit about the response structure.
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%, and the description adds minimal value beyond the schema. The description does not explain how 'months' or 'series' affect the output, though the schema descriptions are clear. A score of 3 is baseline for high schema coverage with slight added context.
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 states the verb 'Get', the resource 'U.S. CPI inflation data', and the source 'Bureau of Labor Statistics'. It specifies the return values (latest, YoY, MoM change), distinguishing it from sibling tools focused on other economic indicators.
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 use for inflation data but provides no explicit guidance on when to use versus when not to use, nor does it mention alternatives. The context of siblings suggests the domain, but the description lacks direct usage boundaries.
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.