Get national Consumer Price Index (CPI) data.
Returns the CPI-U (All Urban Consumers, All Items) monthly time series.
This is the headline CPI measure used for inflation tracking.
Base period: 1982-84=100.
Args:
start_year: Start year for data (default 2020).
end_year: End year for data (default 2025).
| Name | Required | Description | Default |
|---|---|---|---|
| end_year | No | ||
| start_year | No |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the burden. It adds useful behavioral context about the CPI-U measure and monthly frequency, but it does not disclose limitations, data revision policies, or that it is a read-only operation. This is adequate but not rich.
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 concise and well-structured, front-loading the purpose in the first sentence. The Args section is appropriately formatted and every sentence contributes relevant information.
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 two-parameter data retrieval tool with an output schema, the description covers the core functionality, the metric definition, and the parameter defaults. It is mostly complete, though it could mention edge cases like start_year exceeding end_year.
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 0% description coverage, so the description must compensate. However, the Args section merely repeats the parameter names and defaults from the schema, without adding semantics like year range validation or data availability constraints. Minimal added value.
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 it retrieves national CPI data, specifically the CPI-U monthly time series, with a base period. This distinguishes it from sibling tools focused on employment, labor force, and unemployment, making its purpose unambiguous.
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 provides clear context for when to use the tool (for inflation tracking with headline CPI), but it does not explicitly mention alternatives or exclusion criteria. Since the sibling tools are topically distinct, the context is sufficient to guide selection.
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.
There is significant overlap between tools, particularly get_county_employment, get_labor_force_data, and get_unemployment_rate, which all provide similar employment/unemployment metrics at different geographic levels. The descriptions help somewhat, but an agent could easily misselect between these tools when seeking employment data.
All tool names follow a consistent verb_noun pattern with 'get_' prefix and descriptive suffixes (e.g., get_county_employment, get_cpi_data). The naming is predictable and follows the same convention throughout the set.
With only 4 tools, the set feels thin for a BLS employment and wages server. While the tools cover key metrics, there are likely missing wage-related tools (e.g., get_wage_data, get_occupation_data) that would be expected from such a domain.
There are significant gaps in coverage for a BLS employment and wages server. The tools focus heavily on employment/unemployment metrics but completely omit wage data, industry breakdowns, occupation statistics, and other core BLS datasets that would be essential for comprehensive economic analysis.