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

Without annotations, the description carries full burden for behavioral disclosure. It only states 'Add a new cue,' implying mutation, but omits details like whether the cue list must exist, validation of cue number uniqueness, error handling, or return 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 concise sentence with no redundant information. While it is efficient, it could benefit from slightly more detail without becoming verbose.

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 tool with 10 parameters and no output schema, the description is too brief. It does not explain return values, prerequisites, or side effects, leaving significant gaps for an agent to use 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 coverage is 100%, so the input schema already describes all 10 parameters thoroughly. 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 states the tool adds a new cue to an existing cue list, using a specific verb and resource. This distinguishes it from sibling tools like update_cue, remove_cue_from_list, or create_cue_sequence.

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. For example, it does not specify whether to use this for single cue addition vs create_cue_sequence for bulk operations.

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 mentions 'analyze' but doesn't specify whether this is a read-only operation, what permissions are needed, if it's computationally intensive, or what the output format might be. For a tool with no annotations, 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 unnecessary words. It's appropriately sized and 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?

Given no annotations and no output schema, the description is incomplete for a tool that performs analysis. It doesn't explain what the analysis produces (e.g., a report, metrics, or visualizations) or any behavioral aspects like performance characteristics. For a 3-parameter tool with no structured support, this leaves too much unspecified.

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 all three parameters thoroughly. The description doesn't add any additional meaning about the parameters beyond what's in the schema (e.g., it doesn't explain what 'cue list structure' entails or how recommendations are generated). 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 ('analyze') and the target ('structure and timing of a cue list'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate this tool from sibling tools like 'optimize_cue_timing' or 'get_cue_list_details', which might have overlapping purposes in cue list analysis.

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. With siblings like 'optimize_cue_timing' (which might adjust timing) and 'get_cue_list_details' (which might retrieve basic info), there's no indication of when this analysis tool is preferred or what specific context it serves.

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 mentions 'analyze' and 'understand capabilities,' which suggests a read-only operation, but doesn't specify if it requires permissions, has rate limits, or what the output format is (e.g., textual report, structured data). For a tool with no annotations, this leaves significant gaps in understanding its behavior and constraints.

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 front-loads the core purpose ('analyze specific fixtures') and adds clarifying detail ('to understand their lighting capabilities'). There is no wasted wording, repetition, or unnecessary elaboration, making it appropriately sized for its informational content.

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 (analysis tool with 3 parameters), no annotations, and no output schema, the description is incomplete. It doesn't explain what 'lighting capabilities' means in practice, how results are returned, or any behavioral traits like error handling. For a tool that likely produces insights or reports, more context is needed to guide effective use by an AI 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 schema fully documents the three parameters (analysisType, fixtureId, fixtureIds) with descriptions and an enum for analysisType. The description adds no additional meaning beyond the schema, such as explaining how 'fixtureId' and 'fixtureIds' interact or what 'lighting capabilities' entail. Baseline 3 is appropriate as 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 ('analyze') and resource ('specific fixtures'), specifying the purpose is to 'understand their lighting capabilities.' It distinguishes from siblings like 'get_fixture_inventory' or 'create_fixture_instance' by focusing on analysis rather than retrieval or creation. However, it doesn't explicitly differentiate from 'analyze_cue_structure' or 'analyze_script,' which might involve different resources.

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, such as needing fixture IDs from 'get_fixture_inventory,' or compare to siblings like 'analyze_cue_structure' for different analysis types. Usage is implied through the action and resource but lacks explicit context or 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool analyzes scripts for lighting information but doesn't describe what the analysis entails, how results are returned, or any constraints like processing time or error handling. For a tool with 3 parameters and no output schema, 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 a single, efficient sentence that front-loads the core purpose without unnecessary words. It directly states what the tool does, making it easy to parse and understand quickly. Every word earns its place, with no 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 complexity of analyzing theatrical scripts with 3 parameters, no annotations, and no output schema, the description is incomplete. It lacks details on what 'lighting-relevant information' includes, how analysis results are structured, or any behavioral traits like error handling. This makes it inadequate for an agent to fully understand the tool's operation and output.

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 all parameters ('scriptText', 'extractLightingCues', 'suggestScenes') with clear descriptions. The description adds no additional meaning beyond implying that 'lighting-relevant information' relates to the parameters, but it doesn't explain their interplay or provide examples. Baseline 3 is appropriate as 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: 'Analyze a theatrical script to extract lighting-relevant information.' It specifies the verb ('analyze'), resource ('theatrical script'), and scope ('lighting-relevant information'), which distinguishes it from siblings like 'analyze_cue_structure' or 'generate_scene'. However, it doesn't explicitly differentiate from all siblings, such as 'generate_act_cues' or 'optimize_scene', which might also involve script analysis for lighting.

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, such as needing a script text input, or compare it to siblings like 'analyze_cue_structure' for cue-specific analysis or 'generate_scene' for scene generation. Without this context, an agent might struggle to choose between related 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 of behavioral disclosure. It states the tool creates a sequence but doesn't cover critical aspects like permissions required, whether it's idempotent, error handling, or what the output looks like (no output schema). This is a significant gap for a creation tool with multiple parameters.

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 front-loaded and wastes no space, making it easy for an agent 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 the complexity (5 parameters, nested objects, no output schema, and no annotations), the description is inadequate. It lacks details on behavioral traits, output expectations, and usage context, leaving the agent with insufficient information to invoke the tool confidently in a real-world scenario.

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 80%, providing a solid baseline. The description adds minimal value beyond the schema by implying 'sceneIds' are used to build the sequence, but it doesn't explain parameter interactions (e.g., how 'transitionPreferences' affect the sequence) or provide additional context like format examples.

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 ('create') and resource ('sequence of lighting cues from existing scenes'), making the purpose evident. It distinguishes from siblings like 'generate_scene' or 'update_cue' by focusing on sequence creation from existing scenes, though it doesn't explicitly contrast with tools like 'generate_act_cues' or 'reorder_cues'.

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., needing existing scenes), exclusions (e.g., not for modifying sequences), or comparisons to siblings like 'generate_act_cues' or 'reorder_cues', 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 provided, the description carries the full burden of behavioral disclosure. It states it's a creation tool, implying mutation, but lacks details on permissions, side effects, error handling, or response format. This is a significant gap for a tool with 10 parameters and no output schema.

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 front-loaded and wastes no space, 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 the complexity (10 parameters, no annotations, no output schema), the description is inadequate. It doesn't explain what a 'fixture instance' entails in this domain, how creation interacts with other tools, or what the tool returns. For a mutation tool with rich parameters, 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 schema description coverage is 100%, meaning all parameters are documented in the input schema itself. The description adds minimal value beyond the schema by mentioning 'manufacturer/model details', but it doesn't elaborate on parameter interactions or usage nuances. 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 ('Create a new fixture instance') and specifies the resource ('in a project with manufacturer/model details'), making the purpose understandable. It doesn't explicitly differentiate from sibling tools like 'update_fixture_instance' or 'suggest_channel_assignment', 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 like 'update_fixture_instance' or 'suggest_channel_assignment', nor does it mention prerequisites or context for usage. It's a basic statement of function without operational 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Create a new lighting project' implies a write operation, but it doesn't disclose permissions needed, side effects, error conditions, or what happens on success (e.g., returns a project ID). This is a significant gap for a mutation 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, efficient sentence with zero waste. It's front-loaded with the core action and resource, making it easy to scan and understand quickly without 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?

Given the complexity of a creation tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns, how to handle errors, or any behavioral nuances, leaving the agent with insufficient information to use it effectively beyond the basic 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?

Schema description coverage is 100%, so the schema already documents both parameters ('name' and 'description') with their types and requirements. The description adds no parameter-specific information beyond what the schema provides, such as constraints or examples, so it meets the baseline 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 action ('Create') and resource ('new lighting project'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'create_cue_sequence' or 'create_fixture_instance' that also create resources, so it lacks sibling distinction.

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, context, or exclusions, such as when to use 'create_project' versus 'update_project' (if it existed) or how it relates to sibling tools like 'list_projects' or 'delete_project'.

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 deletes a project and all its data, implying a destructive, irreversible action, but lacks details on permissions, side effects, or error handling. For a high-stakes deletion tool, this is insufficient.

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 that efficiently conveys the core action without unnecessary words. It is front-loaded and wastes no space, 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 destructive nature, lack of annotations, and no output schema, the description is incomplete. It doesn't cover critical aspects like what 'all its data' entails, potential dependencies, or response format. For a deletion operation, more context is needed to ensure safe 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?

The input schema has 100% description coverage, clearly documenting both parameters (projectId and confirmDelete). The description adds no additional meaning beyond what's in the schema, such as explaining why confirmation is required. With high schema coverage, the baseline score of 3 is appropriate.

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 ('Delete') and resource ('a project and all its data'), making the purpose immediately understandable. However, it doesn't differentiate from potential alternatives like 'remove_cue_from_list' or other deletion-like operations among siblings, which would require a 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. It doesn't mention prerequisites, such as needing to confirm deletion, or compare it to other tools like 'remove_cue_from_list' for partial deletions. This lack of context leaves usage unclear.

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 'generates' suggestions, implying a read-only or analysis operation, but doesn't clarify whether this creates persistent data, requires specific permissions, or has side effects. No rate limits, error conditions, or output format are mentioned.

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 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 tool with 5 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what 'cue suggestions' entail, how they're generated from 'script analysis', or what format the output takes. The agent lacks critical context about the tool's behavior and results.

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 all parameters thoroughly. The description doesn't add any meaningful parameter context beyond what's in the schema, such as explaining relationships between parameters or providing usage examples.

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 ('generate cue suggestions') and target ('for an entire act based on script analysis'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'create_cue_sequence' or 'update_cue_list', which appear related to cue management but have different purposes.

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 offers no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, appropriate contexts, or exclusions, leaving the agent to infer usage from the tool name and parameters 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 the full burden of behavioral disclosure. It states the tool generates a scene but doesn't clarify whether this is a read-only operation, if it modifies existing data, what permissions are required, or the output format. For a tool with 6 parameters and no annotations, this leaves significant behavioral gaps, such as whether it creates persistent data or returns a temporary design.

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 front-loads the core purpose without unnecessary words. It directly states what the tool does ('Generate a lighting scene') and the key inputs ('based on script context and design preferences'), making it easy to parse. Every part of the sentence contributes to understanding, with no 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 complexity (6 parameters, nested objects, no output schema, and no annotations), the description is incomplete. It doesn't address behavioral aspects like mutation effects, error conditions, or output format, and it lacks usage guidelines. While concise, it fails to provide sufficient context for an AI agent to confidently invoke this tool without relying heavily on the schema, which has gaps in coverage.

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 67%, with parameters like 'sceneDescription' and 'projectId' well-documented in the schema. The description adds minimal value beyond the schema, mentioning 'script context and design preferences' which map to parameters but without extra details. It doesn't explain interactions between parameters (e.g., how 'sceneType' affects generation) or compensate for the 33% coverage gap, so it meets the baseline for adequate but not enhanced semantics.

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: 'Generate a lighting scene based on script context and design preferences.' It specifies the verb ('Generate') and resource ('lighting scene'), and distinguishes it from siblings like 'optimize_scene' or 'update_scene' by focusing on creation rather than modification. However, it doesn't explicitly differentiate from 'create_cue_sequence' or 'generate_act_cues', which are also generation tools, preventing 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 doesn't mention prerequisites (e.g., needing a project created first), compare it to siblings like 'optimize_scene' (for refining) or 'create_cue_sequence' (for sequential cues), or specify scenarios where it's appropriate. Usage is implied only through the tool name and parameters, lacking explicit 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'Get' and 'analyze', implying a read-only operation, but fails to specify if it requires permissions, has rate limits, returns structured data, or handles errors. For a 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 is front-loaded and wastes no space, making it easy to understand at a glance.

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 does not explain what a 'DMX channel usage map' is, the format of the returned data, or any behavioral aspects like error handling. For a tool that likely returns complex lighting data, more context is needed to guide 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?

The schema description coverage is 100%, with clear descriptions for both parameters ('projectId' and 'universe'). The description adds no additional meaning beyond the schema, such as explaining what a 'DMX channel usage map' entails or how the analysis works. Baseline 3 is appropriate since the schema adequately documents the 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 ('Get') and resource ('DMX channel usage map for a project'), making the tool's purpose understandable. However, it does not explicitly differentiate from sibling tools like 'get_fixture_inventory' or 'get_project_details', which might also retrieve project-related data, 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, such as 'get_fixture_inventory' or 'get_project_details', which might overlap in project analysis. There are no explicit instructions on prerequisites, context, or exclusions, leaving usage decisions unclear.

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 offers minimal behavioral disclosure. It mentions 'query and analyze' but doesn't specify whether this is read-only, what permissions are needed, how results are returned, or any rate limits. For a tool with filtering/sorting capabilities and no annotations, 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 front-loads the core purpose. It avoids redundancy and wastes no words, though it could be slightly more structured by separating query vs. analyze aspects.

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 (4 parameters with nested objects, no output schema, and no annotations), the description is incomplete. It doesn't explain what 'analyze' entails, what 'lookup tables' refers to, or how results are structured. For a query/analysis tool with rich filtering options, more context is needed to guide 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%, so the schema already documents all parameters thoroughly. The description adds marginal value by mentioning 'filtering, sorting, and lookup tables', which aligns with the schema's filterBy and sortBy parameters, but doesn't provide additional syntax or format details beyond what the schema provides. Baseline 3 is appropriate when 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 as 'Query and analyze cues in a cue list with filtering, sorting, and lookup tables', which specifies the verb (query/analyze) and resource (cues in a cue list). It distinguishes from siblings like 'list_projects' or 'get_project_details' by focusing on cue list details, though it doesn't explicitly differentiate from similar tools like 'analyze_cue_structure'.

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, when-not-to-use scenarios, or compare it to siblings like 'analyze_cue_structure' or 'get_project_details'. The agent must infer usage from the purpose 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. It describes a read operation ('Get') but lacks details on permissions, rate limits, pagination, or response format. The description doesn't contradict annotations, but it's insufficient for a tool with no annotation coverage, missing key behavioral traits.

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 front-loads the core purpose without unnecessary words. Every part earns its place by specifying the action, resource, and scope concisely.

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 no annotations and no output schema, the description is incomplete. It doesn't explain what 'capabilities' entail, how results are structured, or any operational constraints. For a tool with three parameters and potential complexity in lighting systems, more context is needed to guide 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%, so the schema fully documents all three parameters. The description adds minimal value by hinting at 'project or globally' which relates to 'projectId', but doesn't provide additional syntax or meaning beyond what the schema already specifies. Baseline 3 is appropriate given 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 'available lighting fixtures and their capabilities', specifying scope with 'for a project or globally'. It distinguishes from siblings like 'get_project_details' or 'get_channel_map' by focusing on fixtures, but doesn't explicitly differentiate from 'analyze_fixture_capabilities' which might overlap.

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 mentioning 'for a project or globally', suggesting context for filtering, but doesn't provide explicit guidance on when to use this tool versus alternatives like 'analyze_fixture_capabilities' or 'get_project_details'. No exclusions or prerequisites are 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states 'Get detailed information,' implying a read-only operation, but doesn't specify what details are included, if authentication is required, or how errors are handled. This is a significant gap 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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded and appropriately sized for a simple retrieval tool, with no wasted content.

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 no annotations and no output schema, the description is incomplete. It doesn't explain what 'detailed information' includes, potential return formats, or error conditions. For a tool in a context with many siblings and no structured output, more context is needed to guide 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?

The input schema has 100% description coverage, with 'projectId' documented as 'Project ID to get details for.' The description adds no additional parameter semantics beyond this, such as format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate.

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 ('detailed information about a specific project'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'list_projects' or 'create_project', which would require specifying it retrieves existing project details rather than listing or creating.

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., needing a project ID), exclusions, or comparisons to siblings like 'list_projects' for overviews or 'create_project' for new projects, leaving usage context unclear.

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 'List all available lighting projects' but doesn't mention whether this is a read-only operation, if it requires authentication, what the return format looks like, or any pagination/rate limits. For a list operation with zero annotation coverage, this leaves critical behavioral traits 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, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy for an agent 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 the lack of annotations and output schema, the description is incomplete for a list operation. It doesn't explain what 'list' returns (e.g., project names, IDs, metadata), how results are structured, or any constraints like permissions or limits. This leaves the agent with insufficient context to use the tool 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 input schema has 100% description coverage, with the single parameter 'includeDetails' documented as 'Include fixture and scene counts'. The description adds no additional parameter information beyond what the schema provides, so it meets the baseline score of 3 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 action ('List') and target resource ('all available lighting projects'), making the purpose immediately understandable. However, it doesn't differentiate this tool from sibling tools like 'get_project_details' or 'get_fixture_inventory' that might also retrieve project-related information, preventing 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. With siblings like 'get_project_details' (likely for specific projects) and 'create_project' (for creation), the agent must infer usage context without explicit direction, which is a significant gap.

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 'optimize' but doesn't explain what that entails—whether it modifies data, requires specific permissions, has side effects like overwriting cues, or what the output might be. This is inadequate for a mutation 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, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded and appropriately sized, with no wasted information, 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 the tool's complexity (a mutation operation with 3 parameters) and lack of annotations and output schema, the description is incomplete. It doesn't cover behavioral aspects like what 'optimize' does, potential side effects, or return values, leaving significant gaps for an agent to understand and use the tool 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?

Schema description coverage is 100%, so the schema already documents all parameters (cueListId, optimizationStrategy, projectId) with descriptions and an enum for strategy. The description adds no additional meaning beyond this, such as explaining the impact of different strategies or parameter interactions, meeting the baseline 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 action ('optimize') and resource ('timing of cues in a cue list'), making the purpose understandable. However, it doesn't distinguish this tool from sibling 'optimize_scene' or 'reorder_cues', which might have overlapping timing-related functions, so it misses full sibling differentiation.

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 'optimize_scene' or 'reorder_cues'. It lacks context about prerequisites, such as needing an existing cue list, and doesn't specify scenarios where this optimization is appropriate, leaving usage unclear.

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 lacks behavioral details. It doesn't disclose whether this is a read-only or destructive operation, what permissions are needed, how long it takes, or what happens to the original scene. The phrase 'optimize' implies mutation but without specifics.

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's front-loaded with the core action, though it could be more specific to improve usefulness without adding length.

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 explain what optimization does, what the output looks like, or potential side effects, leaving significant gaps for an agent to understand the tool's behavior and results.

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 fully documents parameters like 'optimizationGoals' with its enum values. The description adds no extra meaning beyond implying goals guide optimization, but doesn't explain trade-offs or interactions between goals, meeting the baseline 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 'Optimize an existing scene for specific goals' states the verb (optimize) and resource (scene), but it's vague about what optimization entails. It doesn't specify whether this adjusts lighting, timing, or other scene properties, nor does it distinguish from sibling tools like 'optimize_cue_timing' or 'update_scene'.

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., needing an existing scene), exclusions, or comparisons to tools like 'update_scene' or 'optimize_cue_timing', leaving the agent to guess based on 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 provided, the description carries full burden for behavioral disclosure. It states the action is 'Remove,' implying a destructive mutation, but doesn't specify permissions required, whether the removal is reversible, side effects on related data, or error handling. 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, efficient sentence with zero waste—it directly states the tool's purpose without unnecessary words. It's appropriately sized for a simple tool and front-loaded with the core action, 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 the tool's destructive nature (implied by 'Remove'), lack of annotations, and no output schema, the description is incomplete. It doesn't address critical aspects like what happens after removal, error conditions, or return values, leaving the agent with insufficient context 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?

The schema has 100% description coverage, with the 'cueId' parameter clearly documented in the schema. The description adds no additional semantic context beyond what's in the schema, such as format examples or validation rules, so it meets the baseline for high schema coverage without enhancing 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 action ('Remove') and the target ('a cue from a cue list'), providing a specific verb+resource combination. However, it doesn't differentiate itself from sibling tools like 'reorder_cues' or 'update_cue_list' which might also involve cue list modifications, leaving room for ambiguity in tool selection.

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., needing an existing cue list), exclusions, or how it differs from related tools like 'reorder_cues' or 'update_cue_list', leaving the agent without context for proper tool selection.

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 ('reorder multiple cues') but doesn't cover critical traits: whether this is a destructive mutation (likely yes, as it changes order), permission requirements, error handling (e.g., duplicate cue numbers), or response format. For a mutation tool with zero annotation coverage, 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 a single, efficient sentence that directly states the tool's function without unnecessary words. It's front-loaded with the core action ('reorder multiple cues') and specifies the mechanism ('by assigning new cue numbers'), making it easy to parse. Every part of the sentence contributes essential information, earning its place.

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 mutation tool (reordering cues likely involves side effects), lack of annotations, and no output schema, the description is incomplete. It doesn't address behavioral aspects like safety, permissions, or what happens to other cues not in the reordering array. For a tool with 2 parameters and no structured support, more context is needed to guide 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?

The input schema has 100% description coverage, clearly documenting both parameters ('cueListId' and 'cueReordering') with their purposes. The description adds no additional semantic context beyond implying reordering involves 'assigning new cue numbers', which is already covered by the schema's details on 'newCueNumber'. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but doesn't detract either.

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 ('reorder') and resource ('cues'), specifying it involves assigning new cue numbers. It distinguishes from siblings like 'remove_cue_from_list' or 'update_cue' by focusing on reordering multiple cues rather than deletion or single updates. However, it doesn't explicitly differentiate from 'optimize_cue_timing' or 'create_cue_sequence', which might involve ordering aspects, leaving some ambiguity.

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., needing an existing cue list), exclusions, or comparisons to siblings like 'update_cue_list' (which might handle ordering) or 'optimize_cue_timing' (which could involve reordering for timing). Without such context, the agent must infer usage from the name and schema 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool 'suggests' assignments, implying a non-destructive, advisory operation, but doesn't clarify if this is a read-only analysis or if it modifies data (e.g., saves assignments to a project). It lacks details on permissions, rate limits, or what 'optimal' means (e.g., based on efficiency, grouping). The description adds minimal context beyond the basic action.

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: 'Suggest optimal channel assignments for multiple fixtures.' It's front-loaded with the core purpose, has zero wasted words, and is appropriately sized for the tool's complexity. Every part of the sentence contributes to understanding the tool's function.

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 (5 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain what 'optimal' means, how suggestions are generated, what the output looks like (e.g., a list of assignments), or behavioral aspects like whether it's a read-only analysis. For a tool that likely involves algorithmic processing of fixture data, more context is needed to guide an AI 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?

Schema description coverage is 100%, meaning all parameters are documented in the input schema. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain 'fixtureSpecs' or 'groupingStrategy' further). With high schema coverage, the baseline is 3, as the description doesn't compensate but also doesn't detract from the schema's 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 the tool's purpose: 'Suggest optimal channel assignments for multiple fixtures.' It specifies the action ('suggest'), the resource ('channel assignments'), and the scope ('for multiple fixtures'). However, it doesn't explicitly differentiate from sibling tools like 'get_channel_map' or 'create_fixture_instance,' which might also involve channel-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., needing a project or fixture specs), exclusions, or comparisons to siblings like 'get_channel_map' (which might retrieve existing assignments) or 'create_fixture_instance' (which might create fixtures with channels). Usage 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 it updates properties without disclosing behavioral traits. It doesn't mention permissions needed, whether changes are reversible, rate limits, or what happens to unspecified properties (e.g., partial updates). This is inadequate for a mutation 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, efficient sentence that directly states the tool's function without unnecessary words. It's front-loaded and wastes no space, making it easy for an agent 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 the complexity of an 8-parameter mutation tool with no annotations and no output schema, the description is insufficient. It lacks details on behavioral aspects, usage context, and return values, leaving significant gaps for an agent to understand the tool's full implications.

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 fully documents all 8 parameters. The description adds no additional meaning beyond implying that properties can be updated, which doesn't enhance the schema's details. This meets the baseline for high schema coverage but doesn't compensate with extra insights.

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 ('Update') and resource ('properties of an existing cue'), making the purpose unambiguous. However, it doesn't differentiate this tool from sibling tools like 'update_cue_list' or 'update_scene', which also modify cue-related entities, so it doesn't reach the highest 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 doesn't mention prerequisites (e.g., needing an existing cue ID), exclusions, or comparisons to siblings like 'update_cue_list' or 'reorder_cues', leaving the agent with minimal context for selection.

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 'Update' which implies a mutation operation, but doesn't specify required permissions, whether changes are reversible, error conditions, or what happens to unspecified fields. 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 with zero wasted words. It's front-loaded with the core action ('Update cue list') and specifies the updatable attributes ('name or description') directly. Every word earns its place, 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 this is a mutation tool with no annotations and no output schema, the description is incomplete. It doesn't cover behavioral aspects like permissions, side effects, or error handling, nor does it explain return values. For a 3-parameter update operation in a context with multiple sibling tools, more contextual information is needed to be fully 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?

Schema description coverage is 100%, so the schema already documents all three parameters (cueListId, description, name) and their types. The description adds marginal value by confirming that name and description are updatable fields, but doesn't provide additional syntax, constraints, or examples beyond what the schema provides. Baseline 3 is appropriate when 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 verb 'Update' and the resource 'cue list', specifying what can be updated ('name or description'). It distinguishes from siblings like 'update_cue' (which updates individual cues) and 'reorder_cues' (which changes cue order), but doesn't explicitly contrast them. The purpose is specific but lacks explicit sibling differentiation.

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., needing an existing cue list), exclusions (e.g., not for creating new cue lists), or comparisons to siblings like 'create_cue_sequence' or 'update_cue'. Usage is implied from the action but not explicitly defined.

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 tool updates properties, implying mutation, but lacks details on permissions, side effects (e.g., if changes affect related data), error handling, or response format. This is inadequate for a mutation 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, efficient sentence that front-loads the core purpose without unnecessary details. Every word earns its place, making it easy for an agent 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?

For a mutation tool with no annotations and no output schema, the description is incomplete. It lacks behavioral context (e.g., what happens on success/failure), doesn't explain the impact of parameter changes (e.g., 'manufacturer' triggering definition creation), and provides minimal guidance, leaving gaps for the 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 schema fully documents all 9 parameters. The description adds no additional meaning beyond the schema, such as explaining parameter interactions or constraints. 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 verb ('update') and resource ('existing fixture instance'), specifying it's for modifying properties. It distinguishes from siblings like 'create_fixture_instance' by focusing on updates rather than creation, though it doesn't explicitly contrast with other update tools like 'update_cue' or 'update_scene'.

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., needing an existing fixture instance), exclusions, or comparisons to siblings like 'update_cue' or 'update_scene', 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 provided, the description carries full burden for behavioral disclosure. It states this is an update operation (implying mutation) but doesn't describe what happens: whether updates are partial or complete, if they're reversible, what permissions are required, or what the response looks like. For a mutation tool with zero annotation coverage, this leaves significant behavioral gaps unaddressed.

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 states the core purpose without any wasted words. It's front-loaded with the essential information ('Update an existing scene') and adds just enough specificity ('with new values'). Every word earns its place in this minimal but complete statement of function.

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 4 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what 'updating' entails operationally, what happens to unspecified fields, whether there are side effects, or what format the response takes. The agent must rely entirely on the input schema and guess about behavior and outputs.

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 all 4 parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema - it doesn't explain relationships between parameters, provide examples, or clarify edge cases. The baseline of 3 is appropriate when the schema does all the parameter documentation work.

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 ('Update') and resource ('an existing scene'), making the purpose immediately understandable. It distinguishes from sibling tools like 'generate_scene' (create) and 'optimize_scene' (modify algorithmically), though it doesn't explicitly contrast with them. The description is specific about updating with 'new values' rather than just saying 'modify scene'.

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 'update_cue' or 'update_cue_list'. It doesn't mention prerequisites (e.g., needing an existing scene ID) or exclusions (e.g., what can't be updated). The agent must infer usage from the tool name and schema alone, with no explicit context about appropriate scenarios.

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