Real-time economic intelligence for AI agents. CPI inflation, unemployment, GDP, Fed rates, Treasury yields, WTI crude, and natural gas. From BLS, FRED, and EIA.
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Usage analytics
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Average 3.6/5 across 7 of 7 tools scored. Lowest: 2.9/5.
Each tool targets a distinct economic dataset (business patterns, demographics, energy, inflation, jobs, macro, treasury), with no overlapping purposes. An agent can easily select the correct tool for a given query.
All tools follow the exact same 'get_{domain}' pattern, making the naming perfectly predictable and easy to navigate.
Seven tools is well-scoped for an economic data server, covering key areas without being overwhelming or too sparse.
Covers most major US economic indicators (employment, inflation, GDP, demographics, energy, debt). Minor gaps exist (e.g., trade data, stock indices) but core workflows are well-supported.
get_business_patternsAInspectU.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. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries full burden. It discloses that data is from US County Business Patterns and includes counts and payroll, but omits behavioral details such as data source freshness, aggregation level (state vs county), pagination, or any constraints. The scope is partially revealed.
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 only two sentences plus an example, with no extraneous content. The first sentence immediately defines the tool's purpose, and the second provides illustrative queries. Every part serves a 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?
Given three simple parameters and no output schema, the description covers the tool's core function and typical use cases. However, it does not specify the exact return format (e.g., a table or JSON structure) or whether results are aggregated by state/industry. A bit more detail would fully inform the agent.
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 description coverage is 100%, so parameters are well-documented in the schema. The description adds example usage (e.g., California for state) but does not introduce new semantic information beyond what the schema provides (e.g., default values, NAICS code list).
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 data returned (businesses, employees, payroll by NAICS and state) and gives concrete examples like "How many tech companies in California?" It distinguishes this tool from siblings like get_demographics or get_jobs by specifying its unique focus on business patterns.
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 the tool is for business pattern queries but does not explicitly advise when to use it over siblings (e.g., get_jobs for employment only) or when not to use it. Example questions provide context but no direct comparison or exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_demographicsAInspectGet 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. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full burden. It implies a read-only operation ('get'), which is appropriate, but does not disclose additional behavioral traits such as authentication requirements, rate limits, or data freshness. The description is adequate but not comprehensive.
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, no unnecessary words. The description is efficient and front-loaded with the key action and resource, followed by a list of data fields.
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 tool with only 2 parameters and no output schema, the description covers the purpose, geographic levels, and specific data fields. However, it does not describe the output structure (e.g., row format, multiple records) which would be helpful for a complete picture.
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 description coverage is 100%, so the schema already documents both parameters. The description adds the list of returned data fields, which provides context but does not add new meaning to the parameters themselves. Baseline of 3 is maintained.
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. Census demographic data', the geographic levels (state/county), and lists specific metrics (population, income, etc.), distinguishing it from siblings that cover 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 does not provide any guidance on when to use this tool versus alternatives, nor does it mention contexts where it should or should not be used. It lacks explicit usage constraints or recommendations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_energyAInspectGet 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 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided. The description indicates a read operation but does not disclose behavioral details such as data freshness, rate limits, authentication needs, or the format of the returned data. The param description hints that data is weekly, but overall transparency is limited.
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 sentence of 13 words, efficiently stating the tool's purpose and key series. No fluff; every word earns its place.
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?
There is no output schema, so the description should explain what the tool returns. It does not mention return format (e.g., object, array, fields). This lack of output information reduces completeness for an AI agent.
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 description coverage is 100% (both parameters have descriptions). The tool description reinforces the series names but does not add significant new meaning beyond what the schema already provides. Baseline 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 verb 'Get', the resource 'energy prices', the source 'U.S. Energy Information Administration', and lists the specific series (WTI crude, Brent crude, natural gas). This distinguishes it from siblings which cover different economic topics like jobs, inflation, treasury, etc.
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 retrieving energy prices but does not explicitly state when to use this tool versus alternatives, nor does it mention exclusions or contexts where other tools would be preferred. No guidance on prerequisites or limitations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_inflationAInspectGet 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 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description clearly states it returns latest value and changes, implying a read operation. No behavioral traits like rate limits or data freshness are mentioned, but for a simple data retrieval tool this is adequate.
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 concise sentences that immediately state purpose and return values. Every word adds value; no fluff.
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 explains return values clearly. Parameters are well-documented in schema. The tool's place among siblings is evident, and the description covers all necessary context for an agent to use it correctly.
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 description adds no additional semantics beyond what the schema provides, earning the baseline score.
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 'Get U.S. CPI inflation data' with specific return values (latest, YoY, MoM change). Verb and resource are explicit and distinguishable from sibling tools which cover 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?
No explicit guidance on when to use or when not. The description implies usage for inflation data but does not compare to siblings or provide exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_jobsBInspectGet 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 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description lacks behavioral details such as data freshness, permissions, rate limits, or side effects. It only lists available series without disclosing any constraints or error conditions.
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 extraneous words. It effectively communicates the core purpose but 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 the simple tool (2 parameters, no output schema), the description covers the data sources and options. However, it omits output format, time range context for the months parameter, and any usage hints, leaving some gaps.
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 description coverage is 100%, so baseline is 3. The description repeats the series options in natural language, adding minor reinforcement but no new meaning beyond what the parameter descriptions already provide.
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 explicitly states the tool retrieves U.S. labor market data from BLS and lists specific data types (unemployment rate, nonfarm payrolls, etc.). The verb 'get' clearly indicates action, and sibling tools cover other economic domains (e.g., energy, inflation), distinguishing this tool for labor metrics.
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?
Description implies usage for labor market data but does not explicitly guide when to use this tool versus alternatives. No exclusion criteria or comparison to sibling tools like get_inflation or get_macro are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_macroCInspectGet 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 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the behavioral burden. It mentions the data source (FRED) but does not disclose read-only nature, rate limits, authentication needs, or data freshness. The schema already covers the series list, so the description adds little behavioral insight.
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, front-loaded with purpose, and contains no extraneous information. Every word adds value.
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 low complexity, the description omits crucial context like the structure of returned data (e.g., time series format, units) and error handling. With no output schema, these details would help the agent use the results correctly.
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 description coverage is 100%, so the schema already documents both parameters. The description lists the series options, but does not add meaning beyond what the schema provides, such as explaining what 'yield_curve' represents. Thus, it meets the baseline.
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 macroeconomic indicators from FRED and lists example series like GDP and Fed funds rate. It distinguishes from siblings by its broad coverage, but lacks explicit differentiation from more specific 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?
No guidance is provided on when to use this tool vs its siblings. For example, if only Treasury yields are needed, get_treasury might be more appropriate. The description does not mention any prerequisites or context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_treasuryAInspectGet 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 |
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 specifies data source and two series, but does not disclose behavior like rate limits, error handling, or whether results are paginated.
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, well-structured sentence. No unnecessary words, efficiently conveys the tool's 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?
For a two-parameter retrieval tool, the description covers the basic purpose but omits details on return structure, error handling, or pagination. 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 description adds minimal value beyond the schema, e.g., mentioning default series, but does not elaborate on format 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?
The description clearly states it gets U.S. national debt and Treasury interest rates, with specific details like 'daily, to the penny'. It distinguishes from siblings focused on 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?
No explicit guidance on when to use this tool vs. alternatives. The description implies it is for treasury data, but lacks exclusions or comparative context with sibling tools.
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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