US Macroeconomic Data

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, and the description does not disclose any behavioral traits such as authentication requirements, rate limits, or side effects. For a data query tool, more transparency is needed.

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: first defines the tool, second provides concrete examples. No redundant information, well 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?

Given the tool's simplicity (3 simple params, no required fields, no output schema), the description sufficiently covers purpose and typical usage. Lacks explicit return format but is otherwise 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 description coverage is 100%, with each parameter described. The description adds value by interpreting NAICS codes (e.g., 51=Tech/Info) and providing example state codes, enhancing meaning 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?

Description clearly states it retrieves U.S. County Business Patterns data (establishments, employees, payroll) by NAICS and state, with direct example queries. Distinguishes from siblings like get_jobs and get_demographics.

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 through examples but does not explicitly state when to use this tool versus alternatives or provide any exclusion criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full disclosure burden. It states 'Get' suggesting a read operation, but lacks explicit mention of read-only nature, authentication requirements, rate limits, or data freshness. The behavioral traits are minimal.

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 that efficiently conveys the tool's purpose and the types of data returned. Every word is purposeful, and there is no extraneous 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?

Given the tool's simplicity (2 parameters, no output schema, no annotations), the description provides adequate but not thorough context. It covers purpose and data fields but lacks usage guidance and behavioral details, making it minimally acceptable.

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 does not add additional meaning to the 'level' or 'state' parameters beyond what the schema provides; it focuses on the output fields instead.

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', and the scope 'by state or county'. It lists specific data fields (population, median income, etc.), distinguishing it from sibling tools like get_jobs or get_inflation.

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 demographic data but does not provide explicit guidance on when to use this tool versus alternatives. No when-not-to-use conditions or sibling comparisons are given, leaving the agent to infer context from the tool name and fields.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 only states that the tool gets prices, but fails to disclose key behavioral traits such as data frequency, update lag, authentication requirements, or whether the response is a single value or time series. This lack of detail hinders an agent from understanding side effects or constraints.

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 very concise, consisting of two short sentences that front-load the main purpose and list the supported resources. It wastes no words, though it could be slightly more structured with a separator between the purpose and the list.

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 and no annotations, the description should provide more context about the return format, units, or pricing type. It covers the basics but leaves uncertainty about what data exactly will be returned, making it marginally complete for an 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 coverage is 100%, so the baseline is 3. The description adds minimal value over the schema—it repeats the series options ('wti_crude, brent_crude, natural_gas') and mentions 'Weekly data points' for limit, which is already in the schema description. No further context is provided.

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 energy prices from a specific source (U.S. Energy Information Administration) and lists the specific commodities (WTI crude, Brent crude, natural gas). This distinguishes it from sibling tools like get_inflation or get_jobs, which cover 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?

The description implicitly advises using this tool when energy price data is needed. It does not explicitly state when not to use it or provide alternatives, but the clear purpose makes usage straightforward.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It only mentions the data source (BLS) but omits details about mutability, data freshness, rate limits, or response format. A score of 2 reflects this significant gap.

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 extremely concise—two sentences with no filler. The first sentence front-loads the purpose, and the second lists key data types. Every word earns its place, making it highly efficient for an AI agent.

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 simplicity of the tool (2 parameters, no output schema) and the sibling context, the description is mostly complete. It fails to hint at the response format (e.g., JSON), but this is minor for a straightforward data retrieval tool. A score of 4 reflects minor omission.

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 baseline is 3. The description adds minimal value beyond the schema, merely listing available data types which the schema already enumerates in the 'series' parameter description. No further semantic enrichment is offered.

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 U.S. labor market data from BLS and lists specific data types (unemployment rate, nonfarm payrolls, etc.), making the verb and resource unambiguous. It effectively distinguishes from sibling tools like get_inflation and get_treasury by specifying the labor market domain.

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 that the tool is for U.S. labor market data, which implicitly guides when to use it vs. siblings focusing on other economic indicators. However, it lacks explicit exclusions or alternatives, so it falls short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states it retrieves data from FRED but does not mention any behavioral traits such as API rate limits, authentication needs, potential latency, or any side effects. The tool is a read operation, but this isn't explicitly confirmed.

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, concise sentence that immediately states the tool's purpose and lists key indicators. Every word serves a purpose with no 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?

Despite the tool being a data retrieval tool with no output schema, the description fails to explain what the output looks like (e.g., time series, values, dates). It also does not mention pagination or any limits on the returned data. This is a significant gap for an agent to use the tool 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 baseline is 3. The description lists the series options, mirroring the schema's enum values, but does not add new meaning beyond what the schema already provides. No additional context on parameter usage or expected format is given.

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 macroeconomic indicators from FRED and lists example series, making the purpose clear. However, it does not explicitly differentiate from sibling tools like get_inflation or get_treasury, which are more specific, so a slight deduction for lacking sibling distinction.

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 the more specialized siblings (e.g., get_inflation, get_energy). There is no mention of use cases, prerequisites, or when not to use it. The description simply states what it does without usage context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It does not disclose rate limits, data freshness, authentication, or any behavioral traits beyond the basic purpose.

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, efficient, no waste. Every word contributes to the 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?

No output schema, so return format is unexplained. Description lacks context on data range, limit max, or what interest_rates includes. 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 tool description adds minimal extra meaning beyond the schema, so baseline 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 specifies the verb 'Get' and the resource 'U.S. national debt' and 'average interest rates on Treasury securities'. It distinguishes from sibling tools that cover 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?

The description offers no guidance on when to use this tool versus sibling tools, nor does it explain when to choose 'debt' vs 'interest_rates' for the series parameter.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.