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

The annotations declare readOnlyHint=true, so the description appropriately does not dwell on safety. Instead, it adds valuable behavioral context by specifying the return format: 'ready-to-use JSON configs'. This discloses what the agent will receive beyond what annotations indicate.

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 consists of three efficient sentences: purpose ('Get example chart configs'), usage ('Optionally filter by mark type'), and return value ('Returns ready-to-use JSON configs'). Every sentence earns its place with zero 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 the low complexity (single optional parameter), presence of annotations, and 100% schema coverage, the description is sufficient. It compensates for the missing output schema by specifying 'ready-to-use JSON configs' as the return value.

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 and only one parameter, the schema already fully documents the mark_type filter. The description adds the 'optionally' qualifier, reinforcing the optional nature, but this is marginal value added over the schema baseline.

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 retrieves 'example chart configs' with the specific verb 'Get'. While it identifies the resource clearly, it does not explicitly differentiate from siblings like render_chart or validate_chart within the text itself, relying instead on the distinct resource name.

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

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

The description provides implied usage guidance by noting the filter is 'optional', which helps the agent understand the parameter is not required. However, it lacks explicit guidance on when to use this versus siblings like render_chart or list_marks.

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?

Annotations declare readOnlyHint=true. The description adds valuable context that the response includes 'specific properties and defaults' for each mark type, hinting at the return structure despite the absence of an output schema. It does not contradict annotations.

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 the action verb 'List'. Zero wasted words; every phrase serves to clarify scope ('all available') or return content ('properties and defaults').

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

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

For a zero-parameter read-only listing tool, the description adequately covers the tool's purpose and hints at return content (properties/defaults) to compensate for the missing output schema. Could slightly improve by indicating the return format (e.g., array), but sufficient for complexity level.

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 zero parameters, establishing a baseline score of 4 per evaluation rules. No parameter semantic information is required or provided.

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 uses a specific verb ('List') and resource ('mark types'), and clarifies the scope ('all available'). It distinguishes from siblings by focusing on 'mark types' vs themes (list_themes), examples (get_examples), or chart operations (render_chart, validate_chart).

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 states what the tool does but provides no explicit guidance on when to use it versus alternatives like get_examples, or prerequisites for using the results. No when/when-not conditions are specified.

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?

Annotations already indicate read-only and closed-world behavior. The description adds that it returns 'names' rather than full theme objects, and emphasizes 'available' (aligning with openWorldHint=false), but does not describe return format or pagination.

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 nine words with no redundancy. Front-loaded with active verb, every word conveys necessary information about the operation, resource, and scope.

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 simple zero-parameter list operation with good annotations. Could be improved by noting that the returned names are intended for use with render_chart, but sufficient as-is given the low complexity and absence of output schema.

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

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

The input schema has zero parameters with 100% coverage. With no parameters to document, the description appropriately requires no additional parameter explanation, meeting the baseline for this case.

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 provides a specific verb (List), resource (chart theme names), and scope (all available), clearly distinguishing it from siblings like render_chart, validate_chart, and list_marks.

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 word 'available' implies this is for discovering valid options, suggesting usage for theme selection, but there is no explicit guidance on when to use this versus get_examples or how it relates to render_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?

Annotations indicate mutation (readOnlyHint: false); description adds crucial behavioral details: output format (URL), temporal constraint (1h expiration), and consumption model (monthly limit). Does not mention error handling or idempotency, but covers primary operational traits well.

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 sentences, each earning its place: purpose, output characteristics, cost constraints, and configuration requirements. Front-loaded with action verb. No redundancy with schema or annotations.

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?

Strong coverage given no output schema: explains return value (URL), expiration, and quota costs. Single parameter is simple. Minor gap: could mention error behavior or recommend pre-validation given validate_chart sibling exists.

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 (1 param described). Description adds semantic value by specifying that the config JSON must contain a 'format' field (svg/png), which is not inferable from schema's generic 'JSON string' description. Slight ambiguity about whether 'format' is top-level or nested, but provides necessary configuration 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?

Description opens with specific verb 'Render' + resource 'chart' + input method 'JSON config'. Clearly distinguishes from sibling tools: get_examples (retrieval), list_marks/themes (enumeration), validate_chart (validation) by being the actual generation/creation operation.

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 implicit usage guidance through cost warning ('counts as one render against your monthly limit') and output constraints ('expires after 1h'). However, lacks explicit when-to-use guidance versus validate_chart sibling or prerequisites (e.g., 'validate config first').

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?

Annotations establish readOnlyHint=true, but the description adds valuable behavioral context: it specifies the return value pattern (validation errors or confirmation) and clarifies the semantic distinction from rendering, which implies no side effects or resource consumption associated with chart generation.

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 efficiently cover purpose, return behavior, and sibling differentiation. Every clause provides distinct value with no redundancy or filler content.

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

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

For a single-parameter validation tool with complete schema coverage and read-only annotations, the description adequately covers operational context. It could optionally specify which schema version is used, but the current description is sufficient for correct agent invocation.

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 input schema fully documents the 'config' parameter. The description references 'chart config' but does not add additional semantic details (such as expected JSON structure or examples) beyond what the schema already provides, warranting the baseline score.

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

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

The description clearly states the specific action (validate), target resource (chart config), and method (against the schema). The explicit note 'Does not count as a render' effectively distinguishes this tool from its sibling render_chart.

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?

While it doesn't explicitly state 'use this when,' the description provides clear contextual guidance by noting that this operation 'does not count as a render,' implicitly directing users to use render_chart for actual rendering and this tool for pre-validation checks.

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