DOE Energy Information

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

No annotations are provided, so the description carries the burden. It states that the tool returns data and lists the data categories, but it does not disclose potential rate limits, authentication requirements, or behavior on invalid inputs. This is adequate but minimal for a read-only data retrieval 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 efficiently structured: a one-sentence purpose followed by a clear Args section. Every sentence adds value, and the parameter list is formatted for easy scanning.

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 an output schema present and four optional parameters, the description fully covers the necessary usage context. It explains the data source, parameter semantics, and return scope. No critical information is missing for a simple data retrieval 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 schema has 0% description coverage, but the description comprehensively explains all four parameters: state with examples, sector with common values, frequency with options and default, and limit with default and max. This fully compensates for the schema's lack of detail.

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 electricity generation, consumption, or price data from the EIA, with specific data types (production, retail sales, prices, fuel consumption). It is distinct from sibling tools which target natural gas and petroleum, so the resource is unambiguous.

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 clear context that this is for electricity data from EIA and provides parameter usage examples. However, it does not explicitly state when not to use it or name alternative sibling tools, relying on the sibling tool names to imply the distinction.

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, the description carries the full burden. It discloses return data categories and parameter constraints (max limit, default frequency, optional state). However, it omits behavioral details like authentication, error handling, or data ordering. The max limit and national-state default are useful, but more transparency would be expected.

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 efficient and well-structured: a purpose sentence, a data-category sentence, and a clean Args list. No filler or redundancy; every sentence contributes.

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 covers the core purpose, data types, and all parameters with constraints. An output schema exists, so return values need not be explained. It could mention the US scope explicitly, but 'from the EIA' implies it. Overall, it is sufficiently complete for a simple query 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 description coverage is 0%, so the description fully compensates. It explains each parameter: state (two-letter abbreviation, omit for national), frequency (monthly/annual, default), and limit (default and max). This adds significant meaning beyond the bare schema types and defaults.

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 natural gas production, consumption, or price data from the EIA' with a specific verb and resource. It enumerates the data categories (wellhead prices, marketed production, consumption by sector, pipeline flows), distinguishing it from sibling tools for electricity, petroleum, and state energy profiles.

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 by specifying the exact natural gas data categories available, making it clear when to use this tool over siblings. It does not explicitly name alternatives or provide exclusion criteria, but the context is unambiguous given sibling names.

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?

The description states it 'Returns time series data' and discloses constraints such as default series and maximum limit (5000). With no annotations available, this provides a good but not exhaustive behavioral picture—it doesn't mention API key requirements, rate limits, or error behavior, but the read-only nature is evident.

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 front-loaded with the core purpose, followed by a concise list of parameter details. No redundant or irrelevant sentences exist.

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 combined with the output schema and explicit parameter examples gives a complete picture for using the tool correctly in all common use cases. The sibling context makes the domain boundary obvious.

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?

Even though the input schema has 0% parameter descriptions, the description's Args section fully documents both parameters: series with multiple concrete examples and meaning, and limit with default and maximum. This more than compensates for the schema gap.

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 opens with 'Get petroleum and oil price or production data from the EIA,' which names a specific verb, resource, and data categories. It further enumerates contents (crude oil prices, production, imports, exports, refinery operations), clearly distinguishing it from siblings like get_electricity_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 makes clear this tool is for petroleum/oil data from EIA, so an agent can infer when to choose it over electricity/natural gas tools. It does not explicitly list alternatives or exclusions, but the scope is unambiguous.

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, the description must carry the burden of behavioral disclosure. It does disclose the return scope (production, consumption, prices, expenditures across fuel types), but does not mention potential limitations, data sources beyond EIA, or any edge cases. It provides a reasonable overview but not comprehensive detail.

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 appropriately sized: a clear opening sentence, a brief summary of what's returned, and a parameter format explanation. It is front-loaded with the main purpose and every sentence adds value. No wasted 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?

Given the tool's simplicity (one parameter, no nested objects) and the presence of an output schema (which likely defines return values), the description covers the essential context: the purpose, the data scope, and the parameter format. It could mention EIA-specific caveats, but overall it is complete enough for a competent 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?

The input schema has 0% coverage, leaving the 'state' parameter entirely unexplained. The description fully compensates by specifying the format: 'Two-letter US state abbreviation (e.g. 'CA', 'TX', 'NY').' This is essential information that makes the parameter usable.

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 specific resource ('comprehensive energy profile for a US state from the EIA'). It explicitly distinguishes itself from sibling tools (get_electricity_data, get_natural_gas_data, get_petroleum_data) by covering all fuel types instead of a single one.

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?

It provides a clear usage context: 'Useful for understanding a state's full energy landscape.' It implies when to use this tool (when a comprehensive overview is needed), but does not explicitly mention alternatives or when not to use it. This is still better than vague guidance.

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