US Macroeconomic Data
Server Details
US macroeconomic data: inflation, jobs, GDP, energy, and Treasury yields
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 3.4/5 across 7 of 7 tools scored.
Each tool targets a distinct economic domain: business patterns, demographics, energy, inflation, jobs, macro indicators, and treasury. Descriptions clearly differentiate them with no ambiguity.
All tools follow a consistent 'get_<domain>' pattern using snake_case, making it easy to predict functionality from the name.
7 tools is appropriate for US macroeconomic data, covering major areas without being overwhelming or too sparse.
Covers key indicators like inflation, jobs, GDP, and demographics, but lacks some areas like trade data or consumer confidence, though these are minor gaps.
Available Tools
7 toolsget_business_patternsAInspect
U.S. County Business Patterns — number of businesses (establishments), employees, and payroll by industry (NAICS code) and state. Answers: How many tech companies in California? Total healthcare employees in Texas?
| Name | Required | Description | Default |
|---|---|---|---|
| year | No | Data year (default: 2021) | 2021 |
| naics | No | NAICS industry code: 51=Tech/Info, 52=Finance, 53=Real Estate, 54=Professional Services, 62=Healthcare, 23=Construction, 44=Retail, 72=Food/Hospitality | 51 |
| state | No | 2-letter state code (CA, TX, NY). Omit for all states. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It implies a read-only data retrieval operation but does not explicitly state behavior such as being non-destructive, requiring no authentication, or returning aggregated statistics. The focus is on data scope rather than operational traits.
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 highly concise with two sentences plus example questions. It front-loads the key data source name and purpose, and each sentence adds value. No extraneous information is present.
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 three parameters, comprehensive schema descriptions, and lack of output schema, the description provides sufficient context for the tool's purpose and scope. It could note that it returns aggregated business statistics, but the examples and data source name reasonably imply the output. Overall adequate.
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%, so the schema already documents all parameters. The description adds no additional parameter-level meaning beyond the schema's own descriptions. It does not elaborate on parameter values beyond what is in the schema, so a baseline score of 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 states the tool provides U.S. County Business Patterns data on establishments, employees, and payroll by NAICS industry and state. It includes specific example queries, making its purpose unambiguous and distinct from sibling tools like get_demographics or get_jobs.
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 gives concrete usage examples ('How many tech companies in California?'), guiding agents on when to use the tool. However, it does not explicitly state when not to use it or compare it to alternatives, though the sibling tool names imply differentiation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_demographicsAInspect
Get U.S. Census demographic data by state or county. Population, median income, poverty rate, unemployment, home values, and education level.
| Name | Required | Description | Default |
|---|---|---|---|
| level | No | state (default) or county | state |
| state | No | 2-letter state code (CA, TX, NY). Omit for all states. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must carry full burden. It mentions returned data fields but fails to disclose behavioral traits such as read-only nature, potential rate limits, authentication requirements, or data freshness. Basic safety profile is missing.
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 efficiently conveys purpose and key data points. No wasted words, front-loaded with action and scope.
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?
Tool is simple with 2 parameters and no output schema. Description lists returned fields but omits information on optionality, default behavior, or any side effects. Adequate but not thorough.
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%, so baseline is 3. The description does not add meaningful semantics beyond the schema; it merely summarizes the purpose without elaborating on parameter formats or constraints.
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 action: 'Get U.S. Census demographic data by state or county' and lists specific metrics (population, median income, etc.), distinguishing it from sibling tools focused on other domains.
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?
No explicit guidance on when to use this tool versus siblings like get_jobs or get_inflation. Usage is implied by the data domain but no direct comparisons or when-not conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_energyAInspect
Get energy prices from the U.S. Energy Information Administration. WTI crude, Brent crude, natural gas.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Weekly data points | |
| series | No | wti_crude (default), brent_crude, natural_gas | wti_crude |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description bears full responsibility. It discloses the data source and types but does not mention any behavioral traits such as read-only nature, authentication requirements, rate limits, or side effects.
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 a single, dense sentence that efficiently conveys purpose and key series without extraneous words.
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 data retrieval tool with only two parameters and no output schema, the description provides sufficient information about the data source and available series. It could mention typical response format but is largely complete.
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 schema already describes both parameters (limit and series) with defaults and descriptions. The tool description does not add additional context 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?
The description clearly states the tool gets energy prices from a specific source (U.S. Energy Information Administration) and lists the three supported series (WTI crude, Brent crude, natural gas). This distinguishes it from sibling tools covering different domains.
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?
No explicit when-to-use or when-not-to-use guidance is provided. However, the purpose is clear and sibling tools cover different data types, so usage is implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_inflationAInspect
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 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full burden but only mentions returned data. It does not disclose read-only nature, error behavior, rate limits, or data freshness, leaving gaps in behavioral expectations.
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?
A single, front-loaded sentence that conveys the essential functionality and outputs without any extraneous text, making it highly efficient.
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?
Despite lacking an output schema, the description adequately summarizes return values (latest value, YoY, MoM). More detail on units or format would improve completeness, but it's largely sufficient for a simple data tool.
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 parameter descriptions in the schema are already detailed. The description adds no extra meaning beyond what the schema provides, so baseline score of 3 applies.
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 tool retrieves U.S. CPI inflation data from BLS and specifies the exact outputs (latest value, YoY, MoM), effectively differentiating it from sibling tools like get_energy and get_jobs.
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?
No guidance on when to use this tool versus alternatives (e.g., get_macro or get_treasury) is provided. The description only states what it does, without context for selecting it over siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_jobsBInspect
Get U.S. labor market data from BLS. Unemployment rate, nonfarm payrolls, job openings, and wages.
| Name | Required | Description | Default |
|---|---|---|---|
| months | No | Months of history | |
| series | No | unemployment_rate (default), nonfarm_payrolls, job_openings, avg_hourly_earnings, labor_force_participation | unemployment_rate |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must cover behavioral traits. It only states data sources and examples, lacking details on data freshness, side effects, rate limits, or whether results are cached. Minimal transparency.
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, no wasted words. Could be slightly more structured, but it is concise and 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?
Adequate for a simple data retrieval tool with two parameters and no output schema. However, it does not specify the return format or data granularity, and it could better differentiate from siblings.
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 description adds context by listing some series options, but it omits one (labor_force_participation) from the list. Baseline 3 is appropriate, as description partially extends schema meaning.
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 retrieves U.S. labor market data from BLS, listing specific series (unemployment rate, nonfarm payrolls, job openings, wages). This distinguishes it well from sibling tools like get_inflation or get_treasury.
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 for labor market data but does not explicitly state when to use this tool over siblings or when not to use it. No alternative tools are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_macroCInspect
Get macroeconomic indicators from FRED (Federal Reserve). GDP, Fed funds rate, Treasury yields, housing starts, retail sales.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Data points to return | |
| series | No | gdp, fed_funds_rate (default), treasury_10y, treasury_2y, yield_curve, housing_starts, retail_sales, consumer_sentiment | fed_funds_rate |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description must cover behavior. Only lists indicator names without explaining output format, pagination, data freshness, or whether the tool is read-only. The default parameters are implied but not described.
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, front-loaded with source and purpose. Efficient but could benefit from more structured detail.
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?
Adequate given two parameters and sibling tools, but lacks information on output structure, data frequency, or error handling. For a data retrieval tool, more context would be helpful.
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 provides 100% coverage with descriptions for both parameters (limit and series). The description lists some series options but adds minimal value beyond the schema. Baseline 3 applies.
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 macroeconomic indicators from FRED and lists examples like GDP and Fed funds rate. Does not explicitly differentiate from sibling tools, but sibling names imply scope differentiation.
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?
No guidance on when to use versus siblings. Does not specify prerequisites or exclusions. Agent must infer from context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_treasuryBInspect
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 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It states it gets data 'to the penny' and 'average interest rates', but does not mention whether it is read-only, rate limits, or side effects. The behavior is minimally described.
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 a single, front-loaded sentence with no wasted words. It is appropriately sized for a simple tool, though it could be slightly more structured.
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 no output schema, the description should clarify the return format. It does not describe how data is returned (e.g., array, fields). The 'limit' parameter's effect is also not explained. Overall, incomplete for a tool with no output schema.
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%, so baseline is 3. The description adds context that the 'debt' series includes daily data to the penny, which is not in the schema, adding some value. However, it does not explain the 'limit' parameter beyond what is in 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?
The description clearly states the verb 'Get' and identifies the resource as U.S. national debt and Treasury interest rates. It distinguishes from sibling tools like get_business_patterns by being specific to treasury 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?
No guidance is provided on when to use this tool versus alternatives. The description implies it is for treasury data, but does not explicitly state context, prerequisites, or exclusions.
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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