AgentEcon

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

The description clearly states that the tool retrieves data (establishments, employees, payroll) and the filtering dimensions (industry, state), which is sufficient for a read-only query tool. No annotations are provided, but the description is transparent about expected behavior.

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 with two short sentences front-loading the core purpose and then giving specific example use cases. Every sentence adds value 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?

The description lacks detail about the output format (e.g., whether results are per county or aggregated by state, despite the tool name suggesting county-level data). With no output schema, more explicit description of the returned fields and aggregation level would improve completeness.

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?

While the schema covers all parameters, the description adds significant value by providing concrete examples (e.g., 'How many tech companies in California?') and expanding on NAICS codes with common industry labels. This goes beyond the schema's basic descriptions.

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 returns business patterns data (establishments, employees, payroll) by industry and state, with specific example queries that illustrate its purpose. It distinguishes itself from sibling tools which cover different 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 provides clear context through examples of what the tool answers, but does not explicitly state when to use this tool versus alternatives or when not to use it. The sibling tools cover distinct domains, so the purpose is implied.

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 must carry the full burden. It describes a read operation ('Get') but does not mention any additional behavioral traits such as data freshness, caching, or authentication requirements. However, the tool is simple and non-destructive, so this is adequate but not exceptional.

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, clear sentence that immediately states the purpose and lists key data points. No extraneous information, perfectly concise.

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 has only two simple parameters and no output schema, the description is fairly complete. It covers what the tool does and the main data returned. Minor missing context (e.g., whether data is per capita or aggregated) is acceptable for this straightforward census 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?

The input schema already describes both parameters with 100% coverage. The description adds value by enumerating the specific demographic fields (population, median income, etc.), which helps the user understand the data returned but does not directly enhance parameter documentation.

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 'Get U.S. Census demographic data by state or county' and lists specific data fields, distinguishing it from sibling tools like get_energy or get_inflation which focus 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?

No explicit when-to-use or when-not-to-use conditions are given, but the sibling tools are clearly different in topic, providing implicit context. The description does not exclude alternatives.

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 bears full responsibility for behavioral disclosure. However, it only states what the tool retrieves and does not mention any behavioral traits such as read-only nature, rate limits, data freshness, or potential errors. This lack of transparency could lead an agent to misuse the 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 a single sentence that efficiently communicates the tool's purpose and scope. It is front-loaded with the action 'Get energy prices' followed by specific sources, containing no redundant or 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 low complexity (2 optional parameters, no required fields, no output schema) and good schema coverage, the description adequately conveys the tool's function. It could mention that data is returned in a time series format, but the implicit understanding is sufficient. The context of siblings being unrelated further supports completeness.

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 input schema provides full descriptions for both parameters (limit: 'Weekly data points'; series: enumerations). The description adds no additional semantics beyond restating the series options. With 100% schema coverage, a baseline of 3 is appropriate; the description does not enhance parameter understanding.

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 and specifies the exact sources (WTI crude, Brent crude, natural gas) and data origin (U.S. EIA). It effectively differentiates from sibling tools like get_inflation and get_jobs, which are unrelated data categories.

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 obtaining U.S. energy price data but does not provide explicit guidance on when to use this tool versus alternatives. Since siblings are distinct categories, confusion is minimal, but explicit when-to-use or when-not-to-use instructions are absent.

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 full burden for behavioral disclosure. It only mentions return values without addressing data freshness, authentication requirements, rate limits, or error handling, leaving significant gaps.

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, well-structured sentence that delivers essential information (source and output) without excess words. Every element contributes directly to understanding tool 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 the absence of an output schema and annotations, the description is minimally sufficient for a simple data retrieval tool. It could be improved by noting data retrieval limitations or how results are ordered.

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 input schema provides full descriptions for both parameters (months and series), achieving 100% coverage. The description adds no additional meaning beyond the schema, warranting the baseline score of 3.

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 tool retrieves U.S. CPI inflation data from the Bureau of Labor Statistics and lists the returned metrics (latest value, YoY, MoM). This distinguishes it from sibling tools like get_energy or get_jobs, which cover different 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 usage for CPI data but provides no explicit guidance on when to use this tool versus alternatives like get_energy or get_macro. It lacks when-not-to-use recommendations or context about prerequisites.

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 transparency. It only states that data comes from BLS but does not disclose any side effects, permissions, rate limits, data freshness, or whether it is a read-only operation. This is insufficient for safe agent use.

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 that front-loads the core action and resource. Every word adds value, and there is no redundant or extraneous information. It is perfectly concise.

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 two optional parameters and no output schema, the description covers the basic function but does not explain default behavior (e.g., months=13) or the format of returned data. While likely sufficient for a straightforward API, it leaves some gaps in completeness.

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% for both parameters (months and series), so the baseline is 3. The description adds little beyond the schema: it lists the series options, which matches the schema description. No additional semantic 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 verb 'Get' and the resource 'U.S. labor market data from BLS', and lists specific data series (unemployment rate, nonfarm payrolls, etc.). It effectively distinguishes from sibling tools like get_energy, get_inflation, and get_macro, which cover different economic 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 implies this tool is for labor market data but does not provide explicit guidance on when to use it versus alternatives, nor does it mention when not to use it. The context of sibling tools provides indirect differentiation, but the description lacks direct usage recommendations.

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, and the description only states 'Get... indicators' without disclosing behavioral traits like read-only nature, update frequency, rate limits, or data source constraints. The agent learns nothing about side effects or limitations.

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 one concise sentence followed by a list, front-loading the essential purpose. It contains no filler or redundant information, 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?

With no output schema, the description should explain what the tool returns, but it only lists input series. It does not clarify output format (e.g., time series, values, or data structure) or the meaning of the 'limit' parameter. This leaves significant gaps for effective usage.

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 documented). The description adds a list of series options (GDP, Fed funds rate, etc.) beyond the schema's enum-like list, but provides little extra meaning. Baseline 3 is appropriate as schema handles parameter details.

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, listing specific series like GDP, Fed funds rate, Treasury yields, housing starts, and retail sales. This distinguishes it from siblings like get_energy, get_inflation, 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?

The description provides no guidance on when to use this tool versus alternatives, such as get_inflation or get_jobs. It does not mention prerequisites or context for usage, leaving the agent to infer based on tool names alone.

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 provided. Description fails to disclose update frequency, authentication needs, rate limits, or error behavior. Only states data source and series types.

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. Key information front-loaded: what data is retrieved.

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?

Lacks output schema; description does not clarify return format or structure. Basic coverage of data content but missing details for agent to handle response.

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 covers both parameters fully (100%). Description adds context about daily accuracy and series options, but does not significantly enhance schema information.

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 U.S. national debt and interest rates, with specific detail 'daily, to the penny'. Distinguishable from siblings which cover other economic domains (demographics, energy, 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?

No explicit when-to-use or when-not-to-use guidance. Context implies use for treasury data, but lacks alternative suggestions or exclusion criteria.

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