caruon

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 describes what data is returned but doesn't mention important behavioral aspects like data freshness (how current is 'current'), update frequency, rate limits, authentication requirements, or error conditions. The description is functional but lacks 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 a single, well-structured sentence that efficiently conveys all essential information: action, resource, temporal scope, and output format. Every element serves a purpose with no 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?

For a zero-parameter read-only tool with no output schema, the description adequately covers the core functionality. However, it lacks details about the return format structure, data sources, or potential limitations that would help an agent use the tool effectively. The absence of annotations means the description should provide more operational context.

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 tool has zero parameters with 100% schema coverage, so the schema already fully documents the parameter situation. The description appropriately doesn't discuss parameters since none exist, maintaining focus on the tool's purpose and output.

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'), resource ('UK electricity generation mix'), and scope ('current'), with explicit details about what data is returned ('percentage contribution of each fuel type'). It distinguishes itself from sibling tools by focusing on generation mix rather than intensity 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?

The description implies usage context by specifying 'current' data, suggesting this tool is for real-time or latest generation mix. However, it doesn't explicitly state when to use this versus the sibling intensity tools or mention any prerequisites or limitations for usage.

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 describes the return values (forecast, actual, qualitative index) and data units (gCO2/kWh), which adds useful context. However, it lacks details on potential limitations like rate limits, data freshness, or error conditions, leaving behavioral gaps for a tool with no annotation support.

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 efficiently conveys the tool's purpose, scope, and return values without any wasted words. It is front-loaded with the core action and resource, making it easy to parse and understand quickly.

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 (0 parameters, no annotations, no output schema), the description is adequate but has gaps. It explains what data is returned but does not cover behavioral aspects like data sources, update frequency, or error handling. For a tool with no structured fields, more contextual detail 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?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately does not discuss parameters, focusing instead on output semantics. This meets the baseline for tools with no parameters, as it avoids redundancy and adds value by explaining return data.

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 ('Get') and resource ('current UK national carbon intensity'), distinguishing it from sibling tools like 'get_generation_mix' and 'get_intensity_by_date'. It explicitly specifies the scope (UK national) and what data is retrieved, avoiding tautology with the tool name.

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 specifying 'current' data, suggesting this tool is for real-time or latest intensity values, as opposed to historical data from 'get_intensity_by_date'. However, it does not explicitly state when not to use it or name alternatives, leaving some ambiguity about sibling tool differentiation.

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 an array of time-window entries with forecast and actual values, indicating a read-only operation. However, it doesn't mention error handling, rate limits, authentication needs, or data freshness, which are gaps for a tool with no annotation coverage.

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 concise and front-loaded: two sentences with zero waste. The first sentence states the purpose and scope, and the second explains the return format, all directly relevant to tool selection and invocation.

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 low complexity (1 parameter, no nested objects) and high schema coverage (100%), the description is mostly complete. It clarifies the return format (array with forecast/actual values), compensating for the lack of output schema. However, without annotations, it could better address behavioral aspects like error cases or data availability.

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 100% description coverage, fully documenting the single 'date' parameter with format details. The description adds no additional parameter semantics beyond what the schema provides, such as date range constraints or default behaviors. Baseline 3 is appropriate when the schema does all 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 specific action ('Get UK carbon intensity data'), resource ('for every half-hour period of a given date'), and scope ('Returns an array of time-window entries each with forecast and actual gCO2/kWh values'). It distinguishes from sibling tools by specifying it's for a specific date with half-hour granularity, unlike 'get_intensity' (likely current) or 'get_generation_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 (historical data retrieval for a specific date with half-hour granularity) but doesn't explicitly state when to use this versus alternatives like 'get_intensity' (which might be for current data) or 'get_generation_mix'. No exclusions or prerequisites are mentioned, leaving the agent to infer appropriate contexts.

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