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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool finds cheapest hours but does not disclose critical traits like data source, update frequency, accuracy, rate limits, authentication needs, or error handling. For a tool with no annotations, this leaves significant gaps in understanding its 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 appropriately sized and front-loaded, with two concise sentences that directly state the purpose and provide useful examples. Every sentence earns its place without redundancy or unnecessary details.

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 complexity of electricity pricing data and the lack of annotations and output schema, the description is incomplete. It does not explain what the tool returns (e.g., list of hours, prices), data freshness, or limitations, which are essential for an agent to use it effectively in scheduling decisions.

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 description coverage is 100%, so the schema already documents all parameters (area, count, consecutive). The description does not add any additional meaning or context beyond what the schema provides, such as explaining how 'area' affects results or the implications of 'consecutive'. Baseline 3 is appropriate when the schema does the heavy lifting.

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's purpose with a specific verb ('Find') and resource ('cheapest hours to use electricity'), and it distinguishes from siblings by focusing on cost optimization rather than emissions, prices, or energy mix. The examples (EV charging, laundry, etc.) further clarify the use case.

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 for when to use this tool ('today/tomorrow' and for scheduling appliances), but it does not explicitly state when not to use it or name alternatives among the sibling tools. It implies usage for cost-saving scenarios but lacks explicit exclusions.

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 effectively describes key behavioral traits: real-time nature, update frequency ('Updated every 5 minutes'), and the practical application context. It doesn't mention rate limits, authentication needs, or error conditions, but provides substantial operational context.

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 perfectly front-loaded with the core purpose, followed by operational details and usage context. Every sentence earns its place: the first states what it does, the second provides update frequency, and the third explains practical application. Zero 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?

For a read-only data retrieval tool with no output schema, the description provides excellent context about what data is returned (CO2 intensity), its real-time nature, update frequency, and practical use case. It doesn't describe the return format or structure, but given the tool's relative simplicity and clear purpose, this is reasonably 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?

With 100% schema description coverage, the input schema already documents both parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline expectation but doesn't provide additional semantic 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 specific action ('Get real-time CO2 emission intensity'), resource ('Danish electricity production'), and measurement unit ('g CO2/kWh'). It distinguishes from sibling tools like 'electricity_prices' and 'energy_mix' by focusing specifically on carbon intensity rather than pricing or generation mix.

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 about when to use this tool ('Useful for timing energy-intensive tasks to low-carbon periods'), which implicitly suggests it's for carbon-aware scheduling. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools.

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. It discloses key behavioral traits: it returns hourly prices for specific timeframes (today/tomorrow), includes raw spot prices (excluding taxes/tariffs), and mentions availability constraints ('when available'). However, it omits details like rate limits, error conditions, authentication needs, or data freshness, which are important for a data-fetching 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 front-loaded with the core purpose in the first sentence, followed by clarifying details. Every sentence adds value: the first defines the tool, the second specifies output format and scope, and the third clarifies price composition. It is appropriately sized with zero redundant 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 moderate complexity (data retrieval with parameters), no annotations, and no output schema, the description is adequate but has gaps. It covers purpose, scope, and price details well, but lacks information on return format (e.g., structure of hourly data), error handling, or performance considerations, which would enhance completeness 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 description coverage is 100%, so the baseline is 3. The description adds value by clarifying the temporal scope ('today and tomorrow') and price composition ('raw spot price, not taxes/tariffs'), which contextualizes the parameters beyond the schema's technical descriptions. It doesn't detail parameter interactions but provides useful semantic 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 tool's purpose with specific verbs ('Get') and resources ('Danish electricity spot prices'), including scope ('current and upcoming', 'hourly prices for today and tomorrow'). It distinguishes from siblings by specifying it returns raw spot prices, unlike 'cheapest_hours' (which likely analyzes prices), 'co2_emissions' (different metric), and 'energy_mix' (different data type).

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 context by mentioning 'when available' for tomorrow's data and specifying what's included (raw spot price, not taxes/tariffs), which helps differentiate from potential alternatives. However, it lacks explicit guidance on when to use this tool versus siblings like 'cheapest_hours' (e.g., for price analysis vs. raw data retrieval) or clear exclusions.

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 and does well by disclosing key behavioral traits: real-time nature, 5-minute update frequency, and the specific data components returned. It doesn't mention rate limits, authentication needs, or error handling, but provides useful operational context.

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, scope, components, and update frequency with zero wasted words. Front-loaded with the core functionality.

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 single-parameter read-only tool with no output schema, the description provides good context about what data is returned and update frequency. Could benefit from mentioning return format or data units, but covers the essential operational aspects well.

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 the 'area' parameter. The description doesn't add any parameter-specific information beyond what's in the schema, maintaining the baseline score for high schema coverage.

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 'real-time Danish electricity production mix', specifying the components (wind, solar, conventional, cross-border exchange) and update frequency. It distinguishes from sibling tools like 'electricity_prices' by focusing on production mix rather than pricing.

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 real-time production data, but doesn't explicitly state when to use this tool versus alternatives like 'co2_emissions' or 'cheapest_hours'. No guidance on prerequisites or exclusions is provided.

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