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 adds some context: it describes the batch nature ('multiple plots in one batch operation'), processing behavior ('Each plot configuration is processed independently, and all plots are created in sequence'), and hints at use cases. However, it doesn't cover critical aspects like error handling, performance implications, or output format, leaving gaps for a mutation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded, starting with the core purpose. The bulleted list is efficient for listing use cases, and the final sentence adds behavioral context without redundancy. However, some bullets could be more concise (e.g., 'Creating plots for each category separately' is slightly wordy), preventing a perfect score.

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 (batch mutation with no annotations and no output schema), the description is moderately complete. It covers purpose, usage examples, and processing behavior, but lacks details on error handling, output format, or performance considerations. This makes it adequate but with clear gaps for an agent to rely on.

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 'plots' parameter documented as 'Array of plot configurations (same structure as create_plot).' The description adds no additional parameter semantics beyond this, so it meets the baseline of 3 where the schema does the heavy lifting, but doesn't compensate with extra details like format examples or constraints.

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: 'Create multiple plots in one batch operation.' This specifies the verb ('create') and resource ('plots') with the distinguishing feature of batch processing. However, it doesn't explicitly differentiate from sibling tools like 'create_plot' beyond the batch aspect, which is why it doesn't reach 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 a bulleted list of use cases (e.g., 'Creating plots for all numeric columns in a dataset'), which implies when to use this tool. However, it lacks explicit guidance on when NOT to use it or when to prefer alternatives like 'create_plot' for single plots, and it doesn't mention prerequisites or constraints, keeping it at an implied usage level.

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 what the tool does but lacks critical behavioral details: it doesn't mention whether this creates a file output (implied by output parameter but not stated), what happens on errors, performance characteristics, or any permissions/authentication needs for a tool with 12 parameters and complex functionality.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and well-structured with a clear opening statement followed by bulleted examples. However, the final sentence ('All parameters support extensive customization through nested objects.') is somewhat redundant given the detailed schema, and some example bullets could be more concise.

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 high complexity (12 parameters, nested objects, no output schema), the description is adequate but incomplete. It explains the tool's purpose and provides usage examples, but lacks information about return values, error handling, or the relationship between parameters (e.g., geom vs geoms exclusivity). For such a complex tool without annotations or output schema, more comprehensive guidance 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?

With 100% schema description coverage, the baseline is 3. The description adds some value by explaining the purpose of parameters (e.g., 'specify data sources', 'aesthetic mappings', 'multi-layer plots') and providing usage examples that illustrate parameter combinations, but doesn't add significant semantic detail beyond what's already in the comprehensive schema descriptions.

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 creates a plotnine visualization from data, specifying the verb ('create') and resource ('plotnine visualization'). It distinguishes from siblings by focusing on custom plot creation rather than batch operations, templates, or listings.

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 usage through example scenarios (simple scatter plot, multi-layer plot, etc.), but does not explicitly state when to use this tool versus alternatives like create_plot_from_template or batch_create_plots. It implies usage through examples but lacks explicit exclusions.

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

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

With no annotations provided, the description carries the full burden. It discloses that templates handle plot configurations and overrides are possible, which adds useful context about behavior. However, it doesn't mention permissions, rate limits, error conditions, or what happens when invalid data is provided, leaving gaps for a mutation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and front-loaded: the first sentence states the core purpose, followed by a bulleted list of template examples for clarity, and concluding with usage notes. Every sentence earns its place without redundancy, making it 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 tool's complexity (6 parameters, nested objects, no output schema, and no annotations), the description is fairly complete. It explains the template concept, lists examples, and covers key usage aspects. However, it lacks details on output format, error handling, or dependencies on other tools like 'list_plot_templates', leaving minor 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?

Schema description coverage is 100%, so the baseline is 3. The description adds value by explaining that templates provide optimized configurations for specific plot types (listing 9 examples), clarifying what 'template_name' entails, and noting that only data and aesthetics need to be provided while overrides are optional. This enhances understanding beyond the schema's parameter descriptions.

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 creates plots using predefined templates, specifying the verb 'create' and resource 'plot from template'. It distinguishes from siblings like 'create_plot' (generic) and 'batch_create_plots' (multiple) by emphasizing template-based creation with optimized configurations for common plot types.

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: for creating plots with predefined optimized configurations, where you only need to provide data and aesthetics. It mentions you can override template settings if needed, but doesn't explicitly state when NOT to use it or name specific alternatives among siblings like 'create_plot' for custom plots.

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. It discloses that the tool saves configurations to a file for reuse, which implies a non-destructive, persistent storage action. However, it lacks details on permissions, error handling, or file format specifics (beyond JSON), leaving some behavioral aspects unclear.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose in the first sentence, followed by a bulleted list of use cases that are directly relevant and add value. Every sentence earns its place, with no wasted words, making it efficient 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 moderate complexity (3 parameters, no output schema, no annotations), the description is mostly complete. It explains the purpose and use cases effectively, but could benefit from more behavioral details (e.g., file overwriting, error scenarios) to fully compensate for the lack of annotations and 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?

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no additional parameter details beyond what the schema provides, such as explaining the 'config' structure or 'directory' default behavior. This 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 specific action ('Export plot configuration to a JSON file') and resource ('plot configuration'), distinguishing it from sibling tools like 'import_plot_config' (which imports) and 'create_plot' (which creates plots). It provides a precise verb+resource combination that is not tautological with the tool name.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly lists use cases (recreating plots, sharing, version control, templates), which provides clear context for when to use this tool. However, it does not specify when NOT to use it or name explicit alternatives (e.g., 'create_plot_from_template' might be a related tool), missing full sibling differentiation.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It adequately describes the core behavior (importing a saved config and creating a plot) and mentions the override capability. However, it lacks details about potential side effects (e.g., whether this creates persistent plots, requires specific permissions, or has rate limits), error conditions, or what the output looks like (though no output schema exists).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured in three sentences: first states the overall purpose, second explains the core action, third adds important nuance about overrides. Every sentence earns its place with no wasted words, and key information is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (importing configs with overrides) and no annotations or output schema, the description is adequate but has gaps. It covers the what and how but lacks details about behavioral implications (e.g., whether this is a read-only operation, what happens on failure, or format of created plots). For a tool that presumably creates visualizations, more context about outputs or constraints 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?

Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds marginal value by mentioning 'optional overrides for config parameters (e.g., new data_source)', which provides a concrete example but doesn't significantly expand beyond what the schema provides. This 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 tool's purpose with specific verbs ('import and use', 'load', 'create a plot') and identifies the resource ('saved plot configuration'). It distinguishes from siblings like 'export_plot_config' (which saves configurations) and 'create_plot' (which creates from scratch rather than from saved configs).

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: when you have a previously exported configuration and want to create a plot from it. It mentions the optional ability to override parameters, which adds useful guidance. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the siblings (e.g., 'create_plot' for starting fresh, 'create_plot_from_template' for template-based creation).

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 describes the organization of palettes by category and hints at output content ('preview colors'), but does not specify critical details such as whether the list is paginated, the format of the preview colors, potential rate limits, or error conditions. For a tool with no 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 well-structured and appropriately sized, starting with a clear purpose statement followed by a bulleted list of categories and a usage note. Each sentence adds value without redundancy. It could be slightly more front-loaded by integrating the usage hint earlier, but overall it is 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 tool's low complexity (1 optional parameter, no output schema, no annotations), the description is moderately complete. It covers the purpose, parameter semantics, and usage context adequately. However, it lacks details on behavioral aspects (e.g., output format, error handling) and does not fully leverage the absence of annotations to provide comprehensive guidance, leaving some gaps for 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?

The input schema has 100% description coverage for its single parameter ('category'), which is documented as an optional filter with enumerated values. The description adds value by listing the specific categories (e.g., 'colorblind_safe', 'scientific') and their purposes, providing semantic context beyond the schema's basic description. However, since the schema already covers the parameter well, the description's contribution is moderate, aligning with the baseline score.

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: 'List available color palettes with preview colors.' It specifies the verb ('List') and resource ('color palettes'), and mentions 'preview colors' as an output detail. However, it does not explicitly differentiate this tool from its siblings (e.g., 'list_geom_types', 'list_plot_templates'), which are also listing tools in the same domain, so it falls short of 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 implied usage context by listing palette categories and stating 'Use these palettes by adding a scale configuration to your plot,' which suggests integration with plotting tools. However, it lacks explicit guidance on when to use this tool versus alternatives (e.g., 'list_themes' or other listing tools), and does not mention any prerequisites or exclusions, leaving room for ambiguity.

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 lists geometry types but doesn't describe traits like whether it returns a static list, requires permissions, has rate limits, or how results are formatted. This leaves significant gaps for a tool that might be used in plot creation workflows.

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 is appropriately sized and front-loaded, 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 tool's simplicity (0 parameters, no annotations, no output schema), the description is minimally adequate but lacks depth. It doesn't explain what the output looks like (e.g., a list of strings or objects), which is important since there's no output schema. For a listing tool in a plot-related context, more completeness would help the agent use it 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 with 100% schema description coverage, so the schema fully documents the lack of inputs. The description adds no parameter information, which is acceptable here as there are no parameters to explain. A baseline of 4 is appropriate since no compensation is needed for missing param details.

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 all available geometry types') and the resource ('that can be used in plots'), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'list_color_palettes' or 'list_plot_templates' beyond the resource focus, 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, such as whether it should be used for reference before creating plots or in conjunction with other listing tools. It lacks explicit when/when-not instructions or named alternatives, offering only implied usage from the resource 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. It mentions that templates include 'descriptions' and examples of patterns, but does not disclose key behavioral traits such as whether the list is paginated, sorted, or filtered, what the return format is, or any rate limits. This leaves significant gaps in understanding how the tool behaves.

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 two sentences, front-loaded with the core purpose and followed by additional context on what templates provide. Every sentence adds value by clarifying the resource and its utility, with no wasted words or redundancy.

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, no annotations), the description is adequate but incomplete. It covers the purpose and some context on template types, but lacks details on behavioral aspects like output format or usage constraints, which are important even for simple tools.

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 does not need to add parameter semantics, and it appropriately avoids discussing parameters, earning a baseline score of 4 for 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 verb 'List' and resource 'all available plot templates with descriptions', specifying what the tool does. It distinguishes from some siblings like 'create_plot' or 'export_plot_config' by focusing on listing rather than creation or export, but does not explicitly differentiate from 'list_color_palettes' or 'list_themes' which are similar list 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 implies usage by mentioning 'Templates provide preset configurations for common visualization patterns', suggesting it's for discovering visualization options. However, it does not explicitly state when to use this tool versus alternatives like 'suggest_plot_templates' or 'list_geom_types', nor provide exclusions or prerequisites for usage.

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

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

No annotations are provided, so the description carries the full burden. It states this is a list operation, implying it's likely read-only and non-destructive, but doesn't disclose any behavioral traits such as permissions needed, rate limits, output format, or whether it returns a static or dynamic list. 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, efficient sentence that directly states the tool's purpose without any fluff. 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 tool's simplicity (0 parameters, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks context on usage, behavioral traits, or output details. For a list tool, this is the bare minimum, leaving gaps in completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0 parameters with 100% coverage, so there's no need for parameter documentation in the description. The description appropriately doesn't mention parameters, which aligns with the schema. A baseline of 4 is applied since no parameters exist, and the description doesn't add unnecessary details.

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 ('List') and resource ('all available themes for plot styling'), making the purpose immediately understandable. However, it doesn't specifically differentiate this from sibling tools like 'list_color_palettes' or 'list_geom_types' beyond the resource type, which keeps it from 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, context for usage, or how it relates to sibling tools like 'list_plot_templates' or 'suggest_plot_templates', leaving the agent to infer usage scenarios.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes what the tool returns (a comprehensive summary with specific components like dataset shape and statistics), which is valuable. However, it does not mention potential limitations, error conditions, performance characteristics, or authentication needs that might be relevant for a data inspection tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and front-loaded, starting with the core purpose, followed by a bulleted list of return details, and ending with the tool's utility. Every sentence earns its place by adding value without redundancy, making it 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 tool's moderate complexity (2 parameters with nested objects, no output schema, and no annotations), the description is largely complete. It clearly explains the tool's purpose, usage context, and return format. However, without an output schema, it could benefit from more detail on the structure of the returned summary (e.g., format or keys), and it lacks information on error handling or data size limits.

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 fully documents both parameters (data_source and rows). The description does not add any parameter-specific semantics beyond what the schema provides, such as explaining how data_source configuration affects preview behavior or clarifying the rows parameter's impact. Baseline 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('preview and inspect data') and resource ('data'), distinguishing it from sibling tools focused on plot creation, configuration, and listing. It explicitly positions this as a verification step 'before creating plots,' making its role distinct within the toolset.

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 ('before creating plots' to 'verify data loaded correctly and understand its structure'), but does not explicitly state when not to use it or name alternatives among sibling tools. The guidance is helpful but lacks explicit exclusions or comparisons.

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 analyzing data characteristics and recommending templates, but fails to disclose critical behavioral traits such as whether this is a read-only operation, if it requires specific permissions, potential rate limits, or what the output format looks like (e.g., list of templates with metadata). For a tool with no annotations, this is a significant gap, warranting a score of 2.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized with two sentences that efficiently convey the tool's function and parameters. The first sentence states the core purpose, and the second elaborates on the analysis process. There's no wasted text, and it's front-loaded with key information, though it could be slightly more structured (e.g., bullet points for clarity).

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 (data analysis tool with 2 parameters, no output schema, and no annotations), the description is moderately complete. It covers the purpose and parameter roles but lacks details on behavioral aspects, output format, and explicit usage context. Without an output schema, it should ideally hint at return values (e.g., 'recommends suitable templates'), which it partially does but not comprehensively. This results in a score of 3, as it's adequate but has clear 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?

Schema description coverage is 100%, so the schema already documents both parameters ('data_source' and 'goal'). The description adds marginal value by explaining that 'data_source' is analyzed for characteristics and 'goal' is optional for user intent, but doesn't provide additional syntax, format details, or examples beyond what the schema implies. With high schema coverage, the baseline is 3, and the description doesn't significantly exceed this.

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 data and suggest appropriate plot templates.' It specifies the verb ('analyze' and 'suggest') and resource ('plot templates'), and distinguishes it from siblings like 'create_plot' or 'list_plot_templates' by focusing on recommendation rather than creation or listing. However, it doesn't explicitly differentiate from 'preview_data' or other analysis tools, keeping it at 4 instead of 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 implies usage by mentioning it 'examines data characteristics' and optionally a 'user goal,' suggesting it's for data exploration or visualization planning. However, it lacks explicit guidance on when to use this tool versus alternatives like 'preview_data' for initial inspection or 'create_plot_from_template' for direct creation, and no exclusions are provided. This results in a score of 3 for implied but not explicit guidelines.

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