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 mentions the pairing requirement, which is useful context, but doesn't disclose other behavioral traits such as whether this is a one-time setup, if it requires specific permissions, what happens on failure, or if it returns credentials. For an authorization tool with zero annotation coverage, this is inadequate.

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, efficient sentence with zero waste. It front-loads the core purpose and includes essential context (pairing mode) without unnecessary details, making it highly concise and well-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?

Given the tool's complexity (authorization with no parameters) and lack of annotations/output schema, the description is minimally adequate. It covers the pairing requirement but misses details like return values, error handling, or dependencies on other tools (e.g., 'discover_nanoleaf'). For a setup tool, this leaves gaps.

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 adds value by specifying the pairing mode requirement, which compensates for the lack of parameters. Baseline 4 is appropriate for zero-parameter tools when the description provides 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 action ('Authorize connection') and target resource ('Nanoleaf device'), distinguishing it from sibling tools like 'connect_to_ip' or 'discover_nanoleaf'. However, it doesn't explicitly differentiate from all siblings (e.g., 'connect_to_ip' might also involve authorization).

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 ('device must be in pairing mode'), which implicitly distinguishes it from tools like 'get_effects' or 'set_brightness'. It doesn't explicitly state when not to use it or name alternatives, but the pairing requirement offers practical guidance.

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 of behavioral disclosure. It states the action ('connect to') but doesn't explain what this entails—whether it establishes a persistent session, validates connectivity, requires authentication, has side effects like network changes, or what happens on failure. For a connection tool with zero annotation coverage, this leaves critical behavior unspecified.

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, direct sentence with zero wasted words. It's front-loaded with the core action and resource, making it highly efficient and easy to parse.

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 a connection operation (likely involving network interaction and potential prerequisites), no annotations, and no output schema, the description is inadequate. It doesn't cover behavioral aspects like error handling, session management, or integration with sibling tools, leaving significant gaps for an AI agent to use it 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%, with clear descriptions for both parameters (IP address and port with default). The description adds no additional meaning beyond the schema, such as IP format examples or port usage context. Baseline 3 is appropriate when the schema fully documents parameters.

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 action ('connect to') and target resource ('Nanoleaf device at a specific IP address'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'discover_nanoleaf' or 'authorize_nanoleaf', which might also involve connection-related operations.

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. It doesn't mention prerequisites (e.g., whether authorization is required first), when this connection is needed (e.g., before controlling the device), or how it relates to siblings like 'discover_nanoleaf' (which might find IPs).

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 states the action ('Discover') but lacks details on how it works (e.g., network scanning method, timeouts, permissions required), what it returns (e.g., list of devices, errors), or side effects (e.g., network traffic). This is a significant gap for a tool with zero 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 a single, clear sentence that directly states the tool's purpose with no wasted words. It is front-loaded and efficiently communicates the core functionality, making it easy for an agent 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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the discovery entails (e.g., returns device IDs, IPs), how results are formatted, or error handling. For a tool in a suite with multiple device interaction siblings, more context on its role and output is needed to guide the agent effectively.

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 0 parameters, and schema description coverage is 100%, so there are no parameters to document. The description doesn't need to add parameter semantics, and it appropriately doesn't mention any. A baseline score of 4 is applied as it meets the requirement for a parameterless tool without unnecessary 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?

The description clearly states the tool's purpose with a specific verb ('Discover') and resource ('Nanoleaf devices on the network'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'connect_to_ip' or 'get_nanoleaf_info', which might involve device discovery or information retrieval, leaving some ambiguity about its unique role.

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. It doesn't mention prerequisites (e.g., network setup), when it's appropriate (e.g., initial setup vs. runtime), or how it relates to siblings like 'connect_to_ip' or 'authorize_nanoleaf', leaving the agent to infer 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?

With no annotations, the description carries full burden but only states it 'gets' a list, implying a read-only operation. It lacks details on behavior such as whether it returns all effects, if there's pagination, error handling, or if it requires prior setup (e.g., connection). No contradictions exist, but it's 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, efficient sentence with no wasted words. It's front-loaded and directly states the tool's function, making it highly concise and well-structured for its simplicity.

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 (0 parameters, no output schema), the description is minimally adequate. However, it lacks context about the Nanoleaf system (e.g., that effects are for lighting) and doesn't explain the return format, which could be important since there's no output schema.

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 doesn't add parameter details, but this is acceptable given the schema's completeness, warranting a baseline score above 3 for a parameterless tool.

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 resource ('list of available effects'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'get_nanoleaf_info' or specify what 'effects' are in this context (e.g., visual effects for Nanoleaf lights).

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 alternatives. It doesn't mention prerequisites (e.g., whether authorization or connection is needed first), nor does it clarify if this is for querying effects before applying them with 'set_effect'.

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 of behavioral disclosure. It states the tool 'Get information,' implying a read-only operation, but does not specify what information is returned, potential errors (e.g., if device is offline), or any side effects. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior and reliability.

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 with no wasted words. It is front-loaded with the core purpose ('Get information about the Nanoleaf device'), making it easy to parse. Every part of the sentence contributes directly to understanding the tool's function, achieving ideal conciseness.

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 lack of annotations and output schema, the description is insufficient for a complete understanding. It does not explain what information is retrieved (e.g., device state, settings, capabilities) or the return format, leaving the agent uncertain about the tool's output. For a tool with no structured output documentation, the description should compensate more to ensure usability.

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 0 parameters, and the input schema has 100% description coverage (though empty). The description does not need to add parameter details, as there are none to explain. It appropriately avoids unnecessary parameter information, earning a high baseline score for parameter clarity in this 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 action ('Get information') and target resource ('Nanoleaf device'), making the purpose understandable. It distinguishes from siblings like 'get_effects' or 'set_brightness' by focusing on general device info rather than specific attributes or controls. However, it lacks specificity about what information is retrieved (e.g., device status, settings, capabilities), which prevents a perfect score.

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. It does not mention prerequisites (e.g., device must be connected or authorized), nor does it differentiate from siblings like 'discover_nanoleaf' (for finding devices) or 'get_effects' (for listing effects). Without such context, the agent must infer usage from 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 are provided, so the description carries full burden for behavioral disclosure. It states the tool sets brightness but doesn't mention whether this requires prior authentication/connection, what happens if lights are off, or any side effects. For a mutation tool with zero annotation coverage, this leaves significant behavioral 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, efficient sentence that directly states the tool's purpose without any wasted words. It's appropriately sized and front-loaded, making it easy to parse 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 this is a mutation tool with no annotations, no output schema, and multiple sibling tools, the description is incomplete. It doesn't address prerequisites (like needing authorization), behavioral implications, or how it differs from similar tools like 'set_color'. For a tool that modifies device state, more context is needed.

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 description mentions 'brightness' but doesn't add meaningful semantics beyond what the schema already provides (brightness level 0-100). With 100% schema description coverage and only one parameter, the baseline is 3 since the schema does the heavy lifting, and the description doesn't compensate with additional 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 action ('Set') and target resource ('brightness of the Nanoleaf lights'), making the purpose immediately understandable. It doesn't explicitly distinguish from siblings like 'set_color' or 'set_effect', but the specificity of 'brightness' provides implicit differentiation. This is clear but lacks explicit sibling comparison.

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 like 'set_color' or 'set_effect', nor does it mention prerequisites such as needing authorization or connection first. It simply states what the tool does without contextual usage information.

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 of behavioral disclosure. 'Set' implies a mutation operation, but the description doesn't mention whether this requires authentication, what happens if lights are off, rate limits, or error conditions. For a mutation tool with zero annotation coverage, this is a significant gap in transparency.

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, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, with every word earning its place. No structural issues or redundancy are present.

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 this is a mutation tool with no annotations and no output schema, the description is incomplete. It doesn't cover behavioral aspects like authentication needs, error handling, or what happens on success. For a tool that changes device state, more context is needed for safe and effective use.

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 hue and saturation parameters fully documented in the schema (including ranges and descriptions). The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline of 3 where 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 action ('Set') and target resource ('color of the Nanoleaf lights'), making the purpose immediately understandable. It distinguishes this from siblings like set_brightness or set_effect by specifying color manipulation. However, it doesn't explicitly contrast with all siblings, so it's not a perfect 5.

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 like set_effect or set_brightness. It doesn't mention prerequisites (e.g., lights must be on or connected) or exclusions. Without any usage context, the agent must infer when this tool is appropriate.

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 states the tool sets an effect but doesn't describe what happens (e.g., whether it changes light patterns immediately, requires specific permissions, has side effects on other settings, or what happens if the effect name is invalid). For a mutation tool with zero annotation coverage, this leaves significant behavioral 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, clear sentence with zero wasted words. It's front-loaded with the core action and resource, making it easy to parse quickly. Every word earns its place without redundancy or unnecessary elaboration.

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 mutation tool with no annotations and no output schema, the description is incomplete. It doesn't address what the tool returns, error conditions, or behavioral nuances like whether the effect persists after power cycles. Given the sibling tools include get_effects, more context on effect availability or dependencies would be helpful.

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, with the single parameter 'effect' documented as 'Name of the effect to apply'. The description adds no additional parameter semantics beyond what the schema provides, such as examples of effect names or format requirements. 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 action ('Set') and target resource ('effect on the Nanoleaf lights'), making the purpose immediately understandable. It distinguishes from siblings like set_brightness and set_color by specifying 'effect' rather than other light properties. However, it doesn't specify what an 'effect' entails compared to basic color/brightness changes.

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 like set_color or set_brightness. It doesn't mention prerequisites (e.g., whether the device must be connected or turned on first) or indicate what effects are available (though get_effects exists as a sibling). Usage context is implied but not explicitly stated.

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 but only states the basic action. It doesn't disclose whether this requires authentication, what happens if lights are already off, whether it affects all lights or specific ones, or any error conditions. 'Turn off' implies mutation but lacks behavioral details.

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 with zero wasted words. It's perfectly front-loaded with the core action and target, making it immediately understandable without any structural issues.

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 mutation tool with no annotations and no output schema, the description is insufficient. It doesn't explain what 'turning off' entails (power state change? brightness to 0?), doesn't mention prerequisites like authorization or connection, and provides no information about the expected outcome or potential errors.

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 description coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, earning a baseline score of 4 for correctly matching the parameter-free nature of the tool.

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 action ('turn off') and target resource ('Nanoleaf lights'), making the purpose immediately understandable. However, it doesn't differentiate from its sibling 'turn_on_nanoleaf' beyond the obvious on/off distinction, missing an opportunity to clarify scope or limitations.

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 about when to use this tool versus alternatives or prerequisites. While the name implies it's for turning lights off, there's no mention of when this is appropriate versus using set_brightness to 0% or whether the tool requires prior connection/authorization steps.

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 states the action ('turn on') but doesn't describe what this entails operationally—whether it requires specific permissions, if it affects all lights or specific ones, what happens if lights are already on, or any error conditions. For a mutation tool with zero annotation coverage, this leaves significant gaps in understanding the tool's 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 a single, clear sentence with zero wasted words. It's front-loaded with the core action and resource, making it immediately scannable and efficient. Every word earns its place by conveying essential information without redundancy or fluff.

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 output schema), the description is minimally adequate but incomplete. It lacks context about prerequisites (e.g., needing authorization or connection), behavioral details (like what 'turn on' means operationally), and relationships to sibling tools. For a mutation tool in a suite with multiple setup tools, this leaves the agent guessing about proper usage flow.

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 0 parameters, and schema description coverage is 100%, so there are no parameters to document. The description doesn't need to compensate for any parameter gaps, and it correctly implies no inputs are required. This meets the baseline for tools with no parameters, though it doesn't add extra context about implicit assumptions (like which lights are affected).

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 action ('turn on') and target resource ('Nanoleaf lights'), making the purpose immediately understandable. It distinguishes from sibling tools like 'turn_off_nanoleaf' by specifying the opposite action, though it doesn't explicitly differentiate from other power-related tools. The description avoids tautology by not just restating 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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (like needing authorization or connection first), when not to use it (e.g., if lights are already on), or relationships to sibling tools like 'authorize_nanoleaf' or 'connect_to_ip'. The agent must infer usage from context alone.

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