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

With no annotations provided, the description carries full burden. It only states that the tool adds an annotation with an optional arrow, but does not disclose behavioral traits such as whether the input figure is mutated, what the function returns, or any side effects. The return value and state changes are not 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 very concise, consisting of two sentences that convey the core purpose and a key feature. It is front-loaded and efficient, though it could be slightly more informative given the tool's parameter complexity.

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 has 7 parameters, 100% schema coverage, and an output schema, the description is adequate but incomplete. It does not explain the return value, that the figure is manipulated, or that the figure should come from create_chart(). Sibling tools and usage context are absent.

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%, providing baseline. The description adds minimal context by describing the annotation as a 'text box with optional arrow,' which aligns with the 'show_arrow' parameter. It does not add significant meaning beyond what the schema already defines for parameters like x, y, figure, etc.

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 callout annotation to a chart for storytelling, with a text box and optional arrow. It specifies the resource (chart) and action (add callout annotation), but does not explicitly differentiate from the sibling tool 'add_chart_callouts', which may overlap in functionality.

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 'add_chart_callouts', 'add_chart_highlight_zone', or 'add_chart_threshold_line'. There is no mention of prerequisites, context, or when not to use this tool.

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

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

With no annotations, the description partially covers behavior: each callout has an arrow, points structure is given, and it returns an enhanced figure dict. However, it does not disclose whether the input figure is mutated or if there are side effects like overwriting existing annotations. The return type is mentioned but no details on the enhanced dict structure.

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 concise with three sentences: first states purpose, second explains points structure, third states return value. Every sentence is essential and there is no unnecessary text.

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 has 4 parameters, nested objects, and an output schema, the description adequately explains the points structure and return type. It could mention limitations (e.g., number of callouts) or that the figure should be from create_chart(). Overall, it provides sufficient context for a moderately complex tool.

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 provides 100% coverage for parameters, but the description adds meaningful detail: it documents optional fields 'ax' and 'ay' in points (not in schema), specifies the figure comes from create_chart(), and clarifies prefix/suffix as text before/after each callout. This adds value beyond the schema.

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: adding multiple annotation callout boxes to highlight data points. It distinguishes from sibling tools like add_chart_annotation and add_chart_highlight_zone through the specific mention of callout boxes with arrows and the structured point data.

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. There is no mention of prerequisites, such as requiring a figure from create_chart(), nor any exclusion criteria. The description implicitly indicates use for highlighting data points but lacks explicit when-to-use/when-not-to-use guidance.

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

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

No annotations, but description accurately states it adds a shaded zone to a chart figure and returns an enhanced dict. Does not mention potential side effects, but for chart enhancement this is sufficient.

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?

Three sentences, front-loaded with action, purpose, and return value. No unnecessary words.

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

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

Adequately describes the tool's purpose and return. Lacks details on error conditions or figure modification behavior, but acceptable for a chart enhancement tool with 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 has 100% coverage, so description adds no new parameter details beyond what's in the schema. 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?

Description clearly states it adds a shaded vertical highlight zone to a chart, with specific examples (COVID years, crisis periods). Distinguishes from sibling tools like add_chart_annotation and add_chart_threshold_line.

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?

Provides ideal use cases (crisis periods, policy changes) but does not explicitly say when not to use or mention alternative 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?

No annotations provided; description only states action and return type. Does not disclose side effects, in-place mutation vs. new dict, or any other 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?

Three short sentences, front-loaded with the action. Every sentence adds value; no waste.

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 parameter count and output schema existence, the description is mostly complete. Missing explicit comparison with sibling tools that add other chart elements.

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 covers 100% of parameters with descriptions. The tool description adds no extra parameter meaning beyond what is already in the input schema.

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 'Add a horizontal threshold/reference line with label', specifying the verb and resource. It is distinguishable from siblings like add_chart_annotation, but does not explicitly differentiate itself.

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?

Provides example use cases (EU average benchmark, target/goal line, critical threshold), implying when to use. However, no guidance on when not to use or alternatives among siblings.

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 exist, so the description carries full burden. It discloses the return format (scalar JSON with value, column, function) and lists supported functions. It does not cover edge cases (e.g., empty data) but is transparent about core 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?

Two sentences, no redundancy. Purpose is front-loaded, and every sentence adds necessary information (purpose, alias, return format, functions).

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 (single column aggregation) and the presence of a full schema and output schema, the description is complete. It covers purpose, alias, return structure, and supported functions without 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 all parameters. The description adds value by explaining the result format and default function (sum), beyond what the schema provides.

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 states exactly what the tool does: aggregate a single column using a function. It distinguishes itself from siblings like filter_data_tool or group_data_tool by specifying it's a shorthand for transform_data(operation='aggregate') and returns a scalar result.

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 via 'Shorthand for transform_data(operation='aggregate')', suggesting when to use the simpler form. However, it does not explicitly state when not to use it or compare with alternatives like group_data_tool for multiple columns.

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 burden. It discloses theme options and optional features but lacks details on side effects (e.g., overwriting existing theme) or error handling for invalid figures.

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?

Three sentences, front-loaded core purpose, followed by theme options and optional features. No wasted words, but could be slightly more 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 output schema exists, return values need not be explained. However, missing details on preconditions (figure must be from create_chart) and whether the tool modifies in place or returns a new figure.

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 coverage, the description adds value by explaining themes with examples and showing structure for annotations and zones, going beyond the schema's 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 applies a visual theme to a chart figure from create_chart(), lists three themes, and distinguishes itself from sibling tools like add_chart_annotation by offering bulk application.

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 after creating a chart, and mentions optional annotations and zones, but does not explicitly state when not to use it or provide alternatives for individual modifications.

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

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

With no annotations, the description should reveal behavior beyond input/output. It does not mention whether the tool is read-only, how it handles missing values, or any required data formats. Only output structure is provided.

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?

Two sentences, front-loaded with purpose and output fields. No superfluous information.

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

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

Adequate for a straightforward tool with output schema. Missing details on error handling, data requirements, and best practices, but sufficient for basic understanding.

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% with basic descriptions. The tool description adds context by giving examples of benchmarks (EU average, regional, custom), augmenting the 'benchmarks' parameter's meaning. This adds value beyond the schema.

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 states the tool compares data against benchmarks and lists return fields. It gives examples like EU average, regional, custom, which clarifies scope. However, it could be more precise about what 'compare' entails (e.g., statistical 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?

No guidance on when to use this tool versus alternatives like compare_datasets or compute_metrics. The description does not mention prerequisites 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?

Discloses ordering and parameters but doesn't add behavioral details like authentication needs, data scope, or pagination behavior beyond what schema provides. No annotations to supplement.

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

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

Single sentence, front-loaded with action, no 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?

Sufficient for a simple list tool with two parameters and an output schema; lacks mention of return type or dataset scope but adequate overall.

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 already describes both parameters (days, page_size) with ranges and defaults. Description adds no new meaning beyond the schema.

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?

Clearly states the tool discovers newly added/updated datasets and returns them ordered by modification time, distinguishing it from siblings like search_datasets or get_dataset.

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 on when to use this tool versus alternatives (e.g., search_datasets for keyword-based discovery, preview_dataset for a single dataset).

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 provided, so description must disclose behavior. Only mentions output format (HTML) but not side effects, permissions, or data handling. Lacks details on how panels are built.

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?

Two short sentences, front-loaded with action. No extraneous information.

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

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

Despite output schema existing, the description is too minimal for a dashboard builder. Does not cover panel configuration, data sources, or theming.

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%, providing baseline of 3. Description adds that panels can be chart, HTML, or big number, which provides context beyond schema, but doesn't detail panel dict structure.

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?

Clearly states 'Build multi-panel dashboard HTML' with specific panel types (chart, HTML, big number). Distinguishes from sibling tools like create_chart (single chart) or build_infographic.

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?

Implied usage for creating dashboards but no explicit when-to-use or alternatives. Does not mention when not to use or prerequisites.

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

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

With no annotations, the description carries the burden: it mentions output is self-contained HTML, responsive, saved to exports/, and returns fields. However, it omits details like file overwrite behavior, auth requirements, or performance implications.

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 very concise: three sentences covering purpose, output format, and return structure with no redundant information.

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

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

Given 9 parameters, schema descriptions, and existing output schema, the description provides a good overview but lacks details on parameter interplay (e.g., how data maps to chart components) and does not address error conditions or data prerequisites.

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 baseline is 3. The description adds context about output fields (headline, insights) but does not significantly enhance parameter understanding beyond what the schema already provides.

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 'complete infographic HTML' with specific components (headline, big number, chart, insights), and distinguishes itself from siblings like 'create_chart' by emphasizing completeness and self-containment.

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

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

The description implies usage for full infographics but provides no explicit guidance on when to use this tool versus alternatives (e.g., 'create_chart', 'build_dashboard'), nor any when-not-to or prerequisite conditions.

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 discloses the return structure (summary_a, summary_b, correlation, insights) but does not mention behavioral traits like side effects, permissions, or limitations.

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 very concise with two sentences and a return line, no wasted words, well-structured and 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 output schema exists and the tool has 8 parameters (4 required), the description covers purpose, use case, and return format. It could be improved by mentioning prerequisites like dataset alignment, but is largely complete.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds minimal parameter information beyond the schema, only stating the tool compares two datasets without elaborating on 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 tool extracts insights by comparing two related datasets, and mentions correlations, divergences, and rank disagreements. However, it does not explicitly distinguish from the sibling tool 'compare_datasets', which may have a similar purpose.

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?

Provides a usage example ('population vs air quality') indicating ideal use cases, but lacks explicit guidance on when not to use or alternatives.

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 must convey behavioral traits. It describes what the tool shows but does not disclose whether it is read-only, requires authentication, or has any side effects. The read-only nature is implied but not explicit.

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?

Two sentences with no wasted words. First sentence states core action, second lists output details. Front-loaded and efficient.

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 presence of an output schema, the description appropriately skips return value details and instead lists what differences are shown. It covers core functionality but could mention prerequisites like dataset existence.

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% and both parameters have clear descriptions referencing 'search_datasets'. The tool description does not add additional meaning beyond the schema, so baseline 3 applies.

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 'compare' and the resource 'datasets', and lists specific comparison dimensions (publisher, tags, resource count, formats, quality). However, it does not distinguish itself from sibling tool 'compare_cross_dataset', which may lead to confusion about when to use which tool.

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 the tool is for choosing the best dataset by comparing differences, providing a usage context. But it lacks explicit when-to-use or when-not-to-use guidance, and does not mention alternatives like 'compare_cross_dataset'.

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; description does not disclose side effects, authorization needs, or safety profile beyond implying non-destructive compute operation.

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?

Two concise sentences front-loaded with main action and return description, no 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?

Adequately covers inputs, outputs, and computed metrics for a moderate-complexity tool; could mention prerequisites like column presence.

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% with clear parameter descriptions; description adds output structure but does not significantly enhance 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?

Description clearly states verb 'Compute' and lists specific derived metrics (YoY changes, per-capita, growth rates, index) and return structure, distinguishing it from siblings like aggregate_data_tool.

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?

Implies usage when these specific metrics are needed, but no explicit guidance on when not to use or alternatives.

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

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

With no annotations, the description bears full burden. It mentions 'smooth transitions and play/pause' but fails to disclose output format, performance implications, data requirements (e.g., time series), or limitations. The existing output schema is not leveraged for behavioral clues.

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?

Extremely concise single sentence. No wasted words, but the brevity sacrifices completeness. Scores high for conciseness, not for structure.

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 9 parameters, many sibling tools, and an output schema, the description is too sparse. It does not explain animation types, data formatting, or how to use the output. The output schema exists but is not referenced to aid 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?

Schema coverage is 100%, so parameters are well-documented. The description adds no additional meaning beyond what the schema provides, but the schema itself is sufficient. Baseline 3 applies.

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 'create' and resource 'animated charts' with specific features ('smooth transitions and play/pause'). It distinguishes from static chart tools but not from other animated chart variants among siblings.

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 on when to use this tool versus alternatives like 'create_chart' or other chart creation tools. The description does not provide context on preferred use cases 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 burden of behavioral disclosure. It mentions the output structure (filepath, title, rows) and color coding but does not disclose potential side effects (e.g., file creation, permissions, or data persistence). This is adequate but leaves questions about behavior beyond the immediate output.

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 extremely concise with three focused sentences: one defining the chart, one listing ideal uses, and one summarizing the return format. No wasted words; each sentence serves a clear purpose.

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 presence of an output schema (as indicated by context signals) and full schema coverage, the description provides sufficient context for a simple chart creation tool. It mentions return shape and use cases, leaving no obvious gaps for the agent to infer.

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% coverage, so the description adds limited additional meaning beyond parameter names and types. It provides context for the data (directional changes) and colors, but does not elaborate on parameter formats or constraints beyond what the schema already declares.

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 explicitly states it creates an arrow-style chart for showing directional changes, with clear color coding (green=positive, red=negative). It distinguishes itself from sibling chart tools by specifying the chart type and use cases like rankings change, budget surplus/deficit, and growth/decline.

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 ideal use cases (rankings change, budget surplus/deficit, growth/decline), giving context for when to use this tool. However, it does not explicitly contrast with sibling chart tools or state when not to use it, which would enhance decision-making.

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 the return format ({filepath, districts_matched, title}) and notes the bias-avoidance behavior. However, it lacks details on prerequisites, side effects (e.g., file creation), or performance implications.

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 extremely concise: two short sentences and a clear return line. Every word adds value, and the structure is front-loaded with the core purpose.

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 6 parameters (3 required) and the presence of an output schema, the description adequately covers the tool's purpose, key behavior, and return format. It could be more complete with usage hints or prerequisites, but it is sufficient for an agent to understand the tool's role.

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 schema already documents all parameters. The description adds minimal extra meaning beyond stating that circle size corresponds to magnitude (value_column), but does not elaborate on other parameters like theme or filename beyond what the schema already provides.

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 'Bubble map of Serbia' and explains that circle size represents magnitude, specifying the resource and distinguishing it from generic mapping tools. It also mentions avoiding large-district bias, adding unique context.

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

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

The description implies usage for visualizing numeric magnitudes on a map of Serbia but does not explicitly state when to use this tool over siblings like create_serbia_map or when not to use it. No alternatives or exclusions are provided.

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 details chart types and column usage but does not disclose behavioral traits like data size limits, performance, or error handling. It mentions 'default auto' for bins but lacks broader behavioral context.

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

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

The description is concise, with a clear opening sentence and a structured list of basic chart types. It is front-loaded with the core purpose, though the list could be slightly more organized with bullet points for readability.

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 41 parameters and an output schema, the description covers the most common use cases (basic chart types) and references `get_resource_data()` for data input. It does not fully detail all 20+ chart types but is sufficient for typical 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?

With 56% schema description coverage, the description adds significant meaning beyond the schema. It groups chart types and maps columns (e.g., 'x_column', 'y_column') to specific chart behaviors, helping the agent select the right parameters for each chart type.

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 'Create interactive charts from data. Supports 20+ chart types.' and lists basic chart types with their column mappings. This distinguishes it from siblings like `create_animated_chart` or `create_scatter_3d`, providing a specific verb and resource.

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 basic chart types, explaining which columns to use for each type (e.g., 'line: x_column + y_column → time series, trends'). However, it does not explicitly state when not to use this tool versus sibling tools for specialized charts (e.g., 3D, animated).

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

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

With no annotations, the description carries full burden. It discloses interactivity (WebGL, orbit-able), return fields ({filepath, title, rows}), and explains cone coloring by magnitude and sizing by sizeref. Missing details on performance with large datasets or error handling, but adequate for most use cases.

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: first stating what it is, then explaining rendering, differentiation from siblings, ideal uses, and return value. Every sentence contributes value with no redundancy. Approximately 120 words, efficient and 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 14 parameters, 7 required, and output schema present, the description covers purpose, usage, behavior, and return. It doesn't explain the output schema (acceptable since it exists) or error handling, but for a chart creation tool it is sufficiently complete.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining how parameters like sizeref and colorscale are used in the rendering context ('Cones are colored by vector magnitude and sized by sizeref'). This provides integrative context beyond 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 an interactive 3D vector field / quiver plot. It specifies the resource (3D vector field) and action (create) with detailed rendering explanation. It distinguishes itself from scalar 3D charts by emphasizing it encodes a *vector*.

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 ideal use cases: wind/air-flow fields, magnetic/electric fields, fluid-flow simulations, gradient directions. It also provides a clear exclusion by stating 'Distinct from the scalar 3D charts', helping the agent select the correct tool among siblings.

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 key behaviors: creates an HTML table, applies conditional formatting, highlights max/min values, and lists return fields. It does not mention side effects or authentication needs, but for a creation tool this is sufficient.

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 concise with five sentences, no fluff. It front-loads the main action, followed by features, ideal uses, and return format. Every sentence adds value.

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 output schema exists (implied by the return object), the description adequately explains the tool's functionality and output. It could mention handling of large datasets, but overall it is complete for a table creation tool with good schema 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?

The input schema has 100% coverage, so the baseline is 3. The description does not add individual parameter details beyond what the schema provides; it only gives overarching context. Therefore, the description does not significantly enhance 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 'Create a styled, responsive HTML data table with conditional formatting,' providing a specific verb and resource. It distinguishes the tool from siblings, which are primarily chart/map creation tools, by focusing on table generation with formatting.

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 includes ideal use cases ('district statistics, budget breakdowns, top-N listings'), giving context for when to use the tool. However, it does not explicitly mention when not to use or provide alternatives, though siblings are clearly different.

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 provided, so the description carries full burden. It adds useful behavioral details such as color coding (green=increase, red=decrease) and return format, but does not discuss side effects, data requirements, or permissions.

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 three sentences, each adding value: definition, color coding, use cases and return format. No unnecessary words, highly efficient.

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 output schema exists and schema coverage is 100%, the description is complete. It provides examples and return format, covering all necessary context 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 coverage is 100%, and all parameters are described in the schema. The description adds minimal extra meaning beyond the schema, thus 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 'Dumbbell chart: before/after comparison' with specific examples (population 2010 vs 2022, budget planned vs executed), making the tool's purpose unambiguous and distinct from siblings.

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 provides ideal use cases through examples, giving clear context on when to use the tool. However, it does not explicitly state when not to use or differentiate from alternatives like create_slope_chart or create_arrow_chart.

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 behavioral burden. It clearly discloses client-side rendering, WebGL interaction, orbit capability, and that scattered samples are accepted without a regular grid. It also states the return object format. While it doesn't cover rate limits or permissions, for a visualization tool these are sufficient.

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 paragraphs long, with the first paragraph explaining the core function and technical detail, and the second providing ideal use cases. Every sentence adds value, and the key purpose is front-loaded. No unnecessary words.

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

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

Given the tool has 12 parameters and no annotations, the description is fairly complete, covering the algorithm, use cases, and output format. However, it could mention limitations (e.g., performance with large datasets) or what 'rows' in the return value refers to. Overall, it provides sufficient context for an agent to understand the tool's purpose and behavior.

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 description does not need to add much per-parameter detail. The description adds context about how parameters like isomin/isomax define the level set and that the marching cubes algorithm runs client-side, but this is supplementary. A baseline of 3 is appropriate given complete 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 creates an interactive 3D iso-surface from a volumetric scalar field. It specifies the specific verb and resource, and distinguishes from sibling tools by mentioning client-side marching cubes and acceptance of scattered samples without a regular grid.

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 ideal use cases (pollution thresholds, isotherms, etc.) and mentions scattered samples are accepted. However, it does not explicitly state when not to use this tool or provide direct comparisons to sibling tools like create_surface_3d or create_volume_3d.

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 provided, so description carries full burden. Discloses interactivity (WebGL, orbit-able) and return structure ({filepath, title, rows}). However, does not clarify side effects (e.g., file saving) or data handling beyond the return.

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?

Five sentences, well-structured, front-loaded with key information. No unnecessary 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?

Describes purpose, ideal use, return value, and key behavior. Lacks details on parameter constraints (e.g., data must contain columns), but schema covers those. Good overall coverage for a tool with moderate complexity.

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 description adds marginal value. It clarifies that color_column splits points into separate lines, adding context beyond the schema's description. Adequate but not exceptional.

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 it creates an 'Interactive 3D line / trajectory chart' (specific verb and resource). It distinguishes from sibling 3D charts by emphasizing trajectory and splitting by color_column.

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?

Explicit ideal use cases are given (e.g., route through lat/lon/altitude, metric across region × time). Does not explicitly state when not to use or list alternatives, but the examples are clear and helpful.

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 provided; description adds return format and highlighting capability but lacks details on sorting, missing values, or default behavior. Adequate but not comprehensive.

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?

Extremely concise, three sentences with no fluff. Front-loaded with purpose and use cases.

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 8 parameters, full schema coverage, and existence of output schema, the description provides enough context for a straightforward chart tool. Could mention sorting or ranking order but not essential.

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 covers all 8 parameters with descriptions. Tool description adds minimal value beyond the schema, only explaining the highlight feature. Baseline 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 it creates a lollipop chart for ranking, with examples like district population ranking. It distinguishes from sibling chart tools by specifying the chart type and use case.

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

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

Lists ideal use cases (ranking, top-N lists), which guides when to use. Does not explicitly exclude alternatives but gives clear 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 full burden. It states the mesh is interactive (WebGL, orbit-able) and returns {filepath, title, rows}. However, it does not disclose whether the tool is read-only or modifies state, or if there are side effects like file creation. It mentions returning a filepath but doesn't explicitly say it creates a file.

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 concise with three clear paragraphs: core functionality in first sentence, contrast with sibling, use cases, and return value. Every sentence adds value, and the structure is front-loaded with the most important information.

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

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

Given the tool complexity (11 parameters, 4 required) and the existence of an output schema, the description covers the core functionality, use cases, and return type. However, it could mention that the data must contain x, y, z columns (already required by schema) or any prerequisites like the data format. The description is mostly complete 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 description coverage is 100%, so baseline is 3. The description adds context that the tool triangulates scattered points and distinguishes it from create_surface_3d. It explains the alphahull parameter's options (0=convex, >0=alpha, -1=Delaunay) but that is already in schema. It does not add new meaning to parameters like theme, title, filename, colorscale, face_color beyond what schema provides.

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 it creates an interactive 3D mesh from scattered points, specifies the triangulation method, and distinguishes itself from the sibling tool create_surface_3d which requires a regular grid. The verb 'creates' and resource '3D mesh' are explicit.

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 explicit use cases (irregular terrain point clouds, region bounding shapes, sparse 3D samples) and mentions when not to use it (vs create_surface_3d). However, it does not explicitly list when alternatives like create_scatter_3d or create_line_3d might be preferred.

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?

Description discloses return structure (filepath, layer_count, title) and the interactive toggle feature, but does not mention permissions, rate limits, or side effects. No annotations are present, so the description carries the burden for behavioral disclosure, but it misses some aspects like whether the tool saves files or requires authentication.

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 extremely concise: two sentences plus a return format list, all front-loaded. No unnecessary words, every sentence earns 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?

The description covers the main purpose, layer structure, and return values. However, it lacks details on theme options, filename formatting, and data preparation requirements. Despite having an output schema (implied), minor gaps remain for a fully self-contained definition.

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

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

The description adds meaningful structure for the 'layers' parameter beyond the schema's 'List of layer dicts', specifying fields like data, name_column, value_column, label, colorscale. Other parameters have adequate schema descriptions, so the description provides additional valuable 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?

Description clearly states it creates a multi-layer choropleth map with toggle buttons, distinguishing it from sibling map tools like create_bubble_map or create_serbia_map. However, it lacks an explicit verb like 'Creates', but the noun phrase implies creation.

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 explicit guidance on when to use this tool versus alternatives like create_bubble_map or create_serbia_map. The description implies it's for multi-layer choropleths, but does not specify when not to use it or mention any prerequisites.

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

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

With no annotations, the description carries the burden. It discloses orientation (males left, females right) and return structure, but does not mention side effects, permissions, or whether output is overwritten. Some context is provided but not comprehensive.

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?

Extremely concise with three sentences. The first sentence is front-loaded with the key purpose. Every sentence adds value with no filler.

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 has 7 parameters and an output schema, the description covers the core visualization purpose and return fields but lacks details on parameter usage (e.g., data structure, age groups order) and assumes domain knowledge. Adequate but not rich.

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 parameters are well-documented in the schema. The description adds minimal new meaning beyond stating the overall purpose (age × sex distribution). It does not elaborate on how data should be structured or explain theme/filename defaults.

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?

Description clearly states it creates a population pyramid showing age × sex distribution with males left and females right. It distinguishes from sibling chart tools by specifying the visualization type and mentioning it's for census data from RZS.

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?

Implies usage for census demographic data but does not explicitly state when to use this tool over alternatives like create_chart or other chart types. No exclusionary guidance is provided.

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 mentions the return structure but fails to disclose any behavioral traits such as potential file overwrites, permissions needed, or side effects. For a tool with zero annotations, this is insufficient 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 three short sentences plus labeled sections for 'Ideal for' and 'Returns'. It is front-loaded with the tool's core function, every sentence adds value, and there is no redundancy or wasted text.

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 6 parameters, no annotations, and an output schema, the description is moderately complete. It covers return values and provides a usage example, but lacks details on data shape requirements, theme/filename defaults, and potential limitations. Schema descriptions compensate partially, but overall the description could be more informative.

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 documents all 6 parameters. The description adds an example (population+budget+schools+hospitals+air quality) that contextualizes value_columns and category_column, but does not significantly enhance meaning beyond what the schema already states. Baseline 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 tool creates a radar/spider chart for multi-metric comparison, using specific verbs 'create' and 'compare'. It distinguishes from sibling chart tools by naming the chart type and providing a concrete example of comparing districts across indicators.

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 clear use case ('Ideal for: comparing districts on population+budget+schools+hospitals+air quality'), helping the agent understand when to use this tool. However, it does not explicitly state when not to use it or suggest alternative tools, which would strengthen guidance among 60+ siblings.

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?

Lists return fields {filepath, title, flows}, but with no annotations, the description lacks behavioral details such as data processing constraints, performance considerations, or side effects.

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?

Description is 4 lines, front-loaded with purpose, ideal uses, and return values. No unnecessary words.

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

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

Given no annotations but with output schema present, the description adequately covers purpose, usage, and returns. Could be enhanced with more behavioral context but is sufficient.

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 parameters are well-documented. The description adds minimal extra meaning beyond the schema, only providing a high-level example.

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 explicitly states 'Sankey (alluvial) diagram showing flow between categories' and lists ideal use cases, clearly distinguishing it from other chart creation tools in the sibling list.

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?

Provides specific ideal use cases like budget flow and energy distribution, but does not mention when not to use the tool or alternatives among siblings.

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 mentions interactivity, WebGL, and orbit capability, but does not disclose potential performance issues, data size limits, or side effects. The return format is partially described, but lacks detail on the 'rows' field.

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 concise (3 sentences) and front-loaded with the key purpose. It efficiently conveys the tool's capability, ideal uses, and return structure without unnecessary detail.

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 (3D chart with 10 parameters), the description provides a good overview of its capabilities and typical use cases. The output schema exists, so return values are covered, but the description could be more thorough about interactive features.

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 baseline is 3. The description adds minimal extra beyond what's in the schema, only summarizing the optional encodings (color, size, symbol). No additional constraints or examples are provided for 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 it creates an interactive 3D scatter/bubble chart using WebGL, with three numeric dimensions and optional encodings. It distinguishes itself from sibling chart types like create_bubble_map and create_cone_3d by specifying the 3D scatter format.

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 context for ideal use cases (spatial data, district × year × population, exploring three census indicators) but does not explicitly state when not to use this tool or mention alternatives. However, the guidance is clear enough for typical 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?

No annotations are provided, so the description carries full burden. It describes the output pattern (text left, charts right) and mentions parameter defaults, but it does not disclose behaviors like file overwriting, validation of steps, required permissions, or error handling. The presence of an output schema (indicated in context) is not mentioned, and step structure is left undefined.

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, each adding value: the first states the core action, the second provides a concrete pattern reference. No redundant words; it is efficiently scoped.

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 creates a multi-section interactive story, the description covers the basic layout and parameters. However, it lacks details about the step structure (required nested keys), expected behavior with empty steps, and output handling. With an output schema present, some burden is relieved, but step semantics remain underspecified.

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 baseline is 3. The description does not add significant meaning beyond what param descriptions provide—it repeats the purpose but doesn't clarify the structure of 'steps' or usage of other params. The step object is left as 'additionalProperties: true' with no extra guidance.

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 'scroll-driven HTML data story (scrollytelling)' with a specific pattern (text left, charts right). It uses a specific verb 'create' and resource, and the title and description distinguish it from sibling tools like create_chart or build_dashboard.

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 does not provide any guidance on when to use this tool versus alternatives, nor does it mention prerequisites or when not to use it. It only describes what it does, leaving the agent to infer usage context without explicit direction.

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

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

With no annotations, the description fully discloses the mapping behavior, supported name formats, and return values, but could mention any side effects or limitations.

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 brief yet comprehensive, with no redundant sentences, and efficiently uses three paragraphs for key information.

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

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

For a 7-parameter mapping tool with an output schema, the description covers input, output, and special constraints (name formats), making it fully informative.

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%, but the description adds practical context about district name formats and the helper tool, enhancing parameter understanding beyond the schema.

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 it creates a choropleth map of Serbia by 25 administrative districts, which is specific and distinguishes from sibling tools like bubble maps or multi-layer maps.

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

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

The description implies usage for Serbian district maps and provides a helper tool reference, but lacks explicit when-not or alternative tool guidance.

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

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

No annotations exist, so the description carries full burden. It describes the return format ('{filepath, title, rows}') and color meaning, but does not disclose potential side effects, authentication needs, or input constraints beyond schema. Adequate but not exceptional.

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 extremely concise, using four short lines to convey purpose, color legend, ideal uses, and return type. No filler or redundant information; every sentence adds value.

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 (8 parameters, 4 required) and presence of an output schema, the description covers the core concept, usage, and return structure. It lacks details about top_n behavior or ranking logic, but is still reasonably complete for a chart creation tool.

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 each parameter is documented. The description adds high-level context (e.g., color coding for rank change) but does not explain parameter-specific semantics beyond what the schema provides. Baseline 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 tool creates a slope chart for ranking changes between two periods, with specific color coding (green=gained rank, red=lost rank). It distinguishes from sibling chart types and includes concrete use cases (census ranking, budget shifts).

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 explicit ideal use cases (e.g., 'census ranking 2002→2022, budget share shifts, district reorderings'), which helps the agent decide when to use this tool. It lacks explicit 'when not to use' or alternatives, but the context is sufficient.

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 provided, so the description carries full burden. It discloses that the plot is interactive (WebGL, orbit-able), integrates client-side, and returns filepath/title/rows. However, it does not cover potential behaviors like data size limits or rendering performance considerations.

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?

Concise and well-structured: purpose sentence, integration explanation, contrast with sibling, ideal use cases, and return information. Every sentence adds value without 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 complexity (12 params, 7 required) and the presence of an output return description, the description is fairly complete. It explains the concept, differentiates from similar tools, and provides use case guidance. Minor gap: no explicit mention of data format expectations beyond 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?

Input schema has 100% coverage with descriptions for all parameters. The description adds context about diameter encoding local flow magnitude, but this is more about behavior than parameter meaning. Schema descriptions are sufficient, so baseline 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?

Clearly states the tool creates an interactive 3D streamtube plot of a vector field's flow, using specific verbs ('create') and a specific resource ('streamtube 3d plot'). Explicitly distinguishes from the sibling 'create_cone_3d' by explaining the difference between integrated trajectories and discrete arrows.

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?

Provides ideal use cases (wind, ocean currents, magnetic field lines) and contrasts with the sibling tool. However, does not explicitly state when not to use the tool or list alternatives beyond the one sibling mentioned.

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 provided, so description carries full burden. Discloses interactivity (WebGL, orbit-able) and return format. Lacks details on limitations (e.g., handling of missing data) or performance implications, but adequate for typical chart creation.

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?

Concise and front-loaded with key information. The first sentence captures the essence. Could trim the second sentence slightly, but overall efficient.

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?

Covers purpose, usage, and return values (with output schema). Does not address error conditions, input validation (e.g., requirement for complete grid), or performance considerations. Adequate but incomplete for a tool with 8 parameters.

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% (baseline 3). The description adds value by explaining that long-format rows are pivoted into a z-value grid, clarifying relationship between data and columns beyond schema definitions.

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?

Description clearly states it creates an interactive 3D surface chart from gridded data, using specific verbs and resource. It distinguishes from sibling 3D tools (e.g., create_scatter_3d) by mentioning the pivoting of long-format data into a grid.

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?

Provides explicit ideal use cases (temperature/air-quality across city × month, terrain surfaces, optimization landscapes). Does not explicitly state when not to use, but for a specialized tool these are sufficient.

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 provided, but description explains ray-marched rendering runs client-side, accepts scattered samples, and returns filepath/title/rows. Does not mention side effects, but as a create tool, it’s implicitly creating a file.

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 with a lead sentence, technical detail, sibling differentiation, and ideal use cases. Slightly verbose but every sentence adds value.

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 complex tool with 14 params and 5 required, the description covers purpose, behavior, rendering method, ideal use, and return values comprehensively, given the 100% schema coverage 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 has 100% coverage with descriptions for all parameters. Description adds minor context (e.g., 'configurable number of internal iso-surfaces') but does not significantly enrich beyond schema.

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 it renders an interactive 3D volume of a scalar field, using specific verbs like 'render' and 'see-through cloud', and explicitly distinguishes itself from the sibling tool create_isosurface_3d.

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?

Provides clear ideal use cases (pollution clouds, temperature fields) and contrasts with isosurface for when only boundaries matter. Lacks explicit when-not-to-use, but the contrast is strong enough.

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 provided, so description carries full burden. Specifies return format {filepath, title, categories} and mentions normalization behavior for values_column. Lacks details on side effects, permissions, or rate limits.

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?

Extremely concise: a single paragraph with a brief list. Every sentence adds value, no wasted words.

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

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

Given no annotations and 7 parameters, the description explains purpose, usage, and return format adequately. Could benefit from more detail on data requirements or output structure, but sufficient.

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%, baseline 3. Description adds minimal new meaning beyond schema, e.g., 'normalized to fill grid' for values_column, but mostly reiterates field 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?

Clearly states it's for waffle chart (icon grid) for proportional data, specifically 'X out of 100' visualization. Provides examples and distinguishes from other chart tools among siblings.

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?

Mentions ideal use cases like proportional data, but does not explicitly state when not to use or suggest alternatives. Clear context for appropriate use.

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, but description fully discloses what the tool returns (column stats, sample values) and implies read-only analysis. Minor omission: no mention of idempotency or caching.

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?

Four concise sentences, front-loaded with the most important usage context. No wasted words; each sentence adds essential info.

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?

Output schema exists, so description doesn't need to detail return format, but it still lists return content. Tool is well-defined with no missing aspects 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%, but description adds context: 'Row dicts from get_resource_data()' for data param and clarifies sample_size as sample values per column. Also explains return data types.

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?

Description clearly states verb ('Understand') and resource ('data structure'), specifies it's for before charting/transforming, and distinguishes from siblings by listing exact output (column names, types, etc.).

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?

Explicitly says 'ALWAYS use after get_resource_data() and before create_chart()', providing clear sequential context and when-to-use guidance.

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

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

With no annotations, the description carries full burden. It explains that the tool modifies hover information and returns an enhanced figure dict. It does not mention side effects like mutating the original figure or limitations, but the behavior is sufficiently disclosed for typical understanding.

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 extremely concise: two sentences stating purpose, a one-line example, and a return type declaration. Every part is informative with no 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 (5 parameters, output schema exists), the description covers what the tool does, how parameters affect output, and what is returned. Combined with parameter descriptions, it is fully complete for an agent to select and invoke 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% with parameter descriptions. The tool description adds value by explaining how parameters like show_mean, show_rank, unit, and value_column affect the tooltip content, including example formatting. This goes beyond the schema definitions.

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 rich contextual tooltips to Plotly figures. It provides a before/after example and mentions specific enrichments (formatted values, deviation from mean, rank). This distinguishes it from siblings like add_chart_annotation or add_chart_callouts, which serve 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 implicitly suggests using this tool after creating a chart to enhance tooltips. However, it does not explicitly state when to use versus alternative tools or when not to use it. The context is clear enough for typical use, 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?

No annotations provided, so description carries the burden. It discloses the need for kaleido, the return value structure, and error condition. However, it does not explain side effects such as file save location, overwrite behavior, or validation of input figure. Transparency is adequate but incomplete.

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 extremely concise with two sentences plus a return line. Every word adds value: purpose, prerequisite, return structure, and error case. No 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?

With 4 parameters, no annotations, and an output schema (context indicates its existence), the description covers the key aspects: purpose, prerequisite, return format, and error handling. It could mention file save location or naming conventions, but overall completeness is good for a well-parameterized tool.

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 baseline is 3. The description adds no additional meaning to parameters beyond what the schema already provides (e.g., width, height, filename, figure). The prerequisite note is not parameter-specific.

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 'Export a chart to PDF' with a specific verb and resource. It distinguishes from sibling export tools (export_data, export_to_datawrapper, export_visualization) by focusing on PDF format and including a prerequisite note about kaleido.

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 on when to use this tool versus alternatives like export_visualization or export_data. The description mentions a prerequisite (kaleido) but does not provide context for selecting this tool over siblings.

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 must fully disclose behavioral traits. It mentions format dependencies (xlsx requires openpyxl) but omits critical details such as where files are saved, whether they overwrite existing files, authentication requirements, or the return value (despite an output schema existing). The description fails to convey important side effects and limitations.

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 extremely concise: two sentences that front-load the purpose and then list the formats. Every sentence earns its place with no filler 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 (3 parameters, all documented in schema), the description is minimally adequate. It explains formats and data sources but lacks details about file path, overwrite behavior, and permissions. The presence of an output schema is unmentioned. Overall, functional but with 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 context for the format parameter by explaining format characteristics ('csv universal, json API-friendly, xlsx requires openpyxl'), which adds value. However, it does not enrich the data or filename parameters beyond what the schema provides. Overall, moderate added value.

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: 'Save data from get_resource_data() or transform_data() to file.' It specifies the verb 'Save' and the resource 'data to file', and it distinguishes from sibling tools like export_visualization (which exports charts) and export_chart_pdf (which exports PDFs of charts).

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 does not provide any guidance on when to use this tool versus alternatives. It mentions data sources but lacks explicit when-to-use or when-not-to-use instructions. No alternatives are named, and there is no context about excluding other export 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?

Since no annotations are provided, the description carries the full burden. It discloses that the tool creates and publishes a chart, and lists supported chart types. However, it does not mention potential side effects (e.g., overwriting existing charts), rate limits, or failure behavior. The information is adequate but not exhaustive.

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 concise and well-structured: it opens with the core purpose, then provides necessary details (API token requirement and supported chart types). Every sentence adds value with no 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 presence of an output schema (so return values need not be described), and 100% schema coverage, the description covers essential context (environment variable, chart type options). It is mostly complete for an external export tool, though missing error handling details.

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 parameters are already documented. The description adds value by explaining that 'data' should come from get_resource_data() and by listing all supported chart types for the chart_type parameter, enhancing clarity beyond the schema.

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: exporting data to Datawrapper to create charts, publish them, and return embed URLs. It distinguishes from sibling tools like create_chart and export_data by specifying the external platform and embed output.

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 mentions the required environment variable but does not provide guidance on when to use this tool versus alternatives (e.g., create_chart for internal charts or export_data for raw data). No explicit usage scenarios or exclusions are given.

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 provided; description carries full burden. It fails to disclose side effects like file overwriting, permissions, storage location, or error handling. Only states it saves to a file, but no details on return values or behavior, despite output schema existence.

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

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

Single sentence of 9 words, no fluff. Action is front-loaded. Every word serves a purpose.

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 existence of output schema (covers return values) and full parameter descriptions, the minimal description is adequate for a simple file save. However, lacks behavioral details that might be needed for robust usage, such as overwrite behavior or error messages.

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%. Description adds value by clarifying 'Plotly figure dict from create_chart()' for figure, specifying format values as 'html' or 'json', and noting filename is 'without extension'. This goes beyond 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?

Description explicitly states 'Save a chart from create_chart() to a file', specifying the action (save), source (create_chart()), and output (file). This clearly distinguishes from sibling tools like export_data (data export) and export_chart_pdf (PDF specific).

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 on when to use this tool vs alternatives such as export_chart_pdf or export_to_datawrapper. Does not mention prerequisites, when not to use, or context for selection among many export-related siblings.

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 exist, so description carries full burden. It mentions output is sorted by severity and includes severity levels, but lacks info on destructive behavior, auth, rate limits, or performance constraints for large datasets.

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?

Three short paragraphs, each serving a purpose: purpose, output details, usage guidance. Information is front-loaded, but the structure could be tightened by merging usage hint into the purpose sentence.

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 output schema exists and no annotations, description adequately covers the tool's behavior. It explains insight categories, sorting, and prerequisite step. Missing slight nuance on behavior when optional columns are omitted, but overall complete.

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

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

Schema coverage is 100%, baseline 3. Description adds value by specifying that data comes from get_resource_data(), and clarifies the meaning of time_column and entity_column as temporal/entity identifier columns.

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 it extracts surprising findings (extremes, trends, outliers, inequality) and details the output structure (severity, headline, narrative). This distinctively differentiates it from sibling tools like compute_metrics or generate_data_narrative.

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?

Explicitly instructs to use AFTER data_profile() to identify time/entity columns, giving clear usage context. However, it does not explicitly mention when not to use or list alternative tools for other analysis needs.

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