BLS Employment & Wages
Server Details
Unemployment rates, labor force, and Consumer Price Index from the Bureau of Labor Statistics
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.1/5 across 4 of 4 tools scored.
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.
Available Tools
4 toolsget_county_employmentAInspect
Get county-level unemployment rate, employment, and labor force data.
Returns three time series for a specific county: unemployment rate (percent),
employment count, and labor force size.
Args:
state: Two-letter US state abbreviation (e.g. 'WA', 'CA', 'NY').
county_fips: Three-digit county FIPS code as a string (e.g. '033' for King County, WA).
start_year: Start year for data (default 2020).
end_year: End year for data (default 2025).| Name | Required | Description | Default |
|---|---|---|---|
| state | Yes | ||
| end_year | No | ||
| start_year | No | ||
| county_fips | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses that it returns three time series and mentions default years, but lacks information on rate limits, error behaviors, or data freshness.
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?
Well-structured with purpose front-loaded, though the Args section is slightly verbose; every sentence provides necessary 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?
Complete for the tool's complexity; adequately describes inputs and outputs without needing to detail the return structure since output schema exists.
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?
Excellent compensation for 0% schema coverage by providing detailed Args section with formats and examples for all four parameters.
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?
Clearly states the tool retrieves county-level unemployment, employment, and labor force data, distinguishing it from siblings through the 'county-level' specificity.
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?
Provides implied usage through 'county-level' specification but lacks explicit when-to-use guidance versus state/national alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_cpi_dataAInspect
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 |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses crucial interpretation context absent from annotations: monthly time series frequency and the 1982-84=100 base period required to understand index values.
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?
Front-loaded with specific metric details (CPI-U, base period) and uses a concise Args section to clarify parameters without redundancy.
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?
Given the simple two-parameter input and existence of output schema, coverage is sufficient; could explicitly confirm 'US national' scope but context makes this clear.
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?
Compensates effectively for 0% schema description coverage by documenting that parameters filter the date range and restating their default values (2020, 2025).
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?
Clearly states it retrieves national CPI inflation data and distinguishes itself from employment-focused siblings via both metric type (prices vs. employment) and geographic scope (national vs. county).
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?
Implies usage for inflation tracking but lacks explicit guidance on when to select this over labor market data siblings or whether regional CPI alternatives exist.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_labor_force_dataAInspect
Get labor force participation, employment, and unemployment counts for a state.
Returns three time series: labor force size, employment count, and
unemployment count. All values are in thousands of persons.
Args:
state: Two-letter US state abbreviation (e.g. 'WA', 'CA', 'NY').
start_year: Start year for data (default 2020).
end_year: End year for data (default 2025).| Name | Required | Description | Default |
|---|---|---|---|
| state | Yes | ||
| end_year | No | ||
| start_year | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses critical unit information ('thousands of persons') and return structure (three time series) but omits operational details like data frequency, source, or rate limits.
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?
Well-structured with action/front-loaded purpose followed by Args section; includes only essential information without redundancy.
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?
Adequately covers the simple 3-parameter tool's functionality, return format, and required vs optional parameters, though could clarify temporal resolution of data.
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?
Fully compensates for 0% schema description coverage by providing clear parameter semantics: state format with examples, and explicit default values for year ranges.
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?
Clearly states it retrieves labor force counts (not rates) for a state, distinguishing it from get_unemployment_rate (rates) and get_county_employment (county-level).
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?
Implies usage through return value specification (counts vs rates, state-level) but lacks explicit 'when to use' guidance contrasting with siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_unemployment_rateAInspect
Get unemployment rate time series from BLS LAUS data.
Returns monthly unemployment rates for a state or county. Data is returned
in chronological order with year, period, and percentage value.
Args:
state: Two-letter US state abbreviation (e.g. 'WA', 'CA', 'NY').
county_fips: Optional 3-digit county FIPS code (e.g. '033' for King County).
If provided, returns county-level data; otherwise state-level.
start_year: Start year for data (default 2020, min 4-digit year).
end_year: End year for data (default 2025).| Name | Required | Description | Default |
|---|---|---|---|
| state | Yes | ||
| end_year | No | ||
| start_year | No | ||
| county_fips | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses return format (chronological order), frequency (monthly), data source (BLS LAUS), and field contents (year, period, percentage) beyond empty annotations.
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?
Front-loaded with purpose, followed by return value description, then structured Args section; every sentence provides essential information without redundancy.
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?
Adequately covers the simple retrieval pattern; mentions output structure despite presence of output schema, though could note data latency or revision policies typical of BLS data.
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?
Compensates for 0% schema description coverage by providing detailed Args section with examples (e.g., 'WA', '033') and behavioral logic (county_fips presence determines granularity).
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?
States specific action (Get) and resource (unemployment rate time series from BLS LAUS), clearly distinguishing from siblings get_county_employment (employment vs unemployment), get_cpi_data (price index), and get_labor_force_data (labor force levels vs rates).
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?
Provides clear context that this retrieves unemployment rates specifically, but does not explicitly contrast with get_labor_force_data or state when to prefer state vs county granularity.
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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