Vlak

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

Annotations already disclose readOnly, idempotent, and non-destructive behavior, so the description does not need to restate it. It adds the kebab-case naming convention and lists what the result contains, but that list is largely return-value information which the output schema already covers. No auth, error, or rate-limit behavior is described.

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: the first front-loads what the tool returns and the second is a crisp, actionable instruction. There is no redundant text or tautology, and both sentences earn their 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?

For a single-parameter, read-only retrieval with an output schema and full annotations, the description is adequate. It explains the resource returned and the valid source of the parameter, though it stops short of describing behavior with invalid or nonexistent names.

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 already documents 'name' with examples, so the baseline is 3. The description adds extra meaning by requiring the kebab-case format and directing the agent to use names from list_components or search_components, which provides provenance and format guidance 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 a specific retrieval operation (get the markdown docs page for a component) and enumerates the returned resource pieces: props JSON, CSS snippet, React example, classes, and aliases. This distinguishes it from siblings like list_components, search_components, and get_guide.

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 tells the agent to pass the kebab-case name from list_components or search_components, which establishes a clear input source and implicitly says when to use this tool (once a specific component is known). It does not explicitly explain when to choose it over get_guide, get_install, or get_tokens, but the resource type is clear 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds the ordering convention and page-choice semantics but does not disclose additional runtime behavior; since an output schema exists, this is only a minor gap and there is no contradiction.

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 purposeful sentences with no filler. The critical ordering instruction is front-loaded, and the enum mappings are packed compactly into a single clause. Every part of the description 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 tool has only one optional parameter, the schema covers it fully, the description explains every enum value, annotations cover safety and idempotency, and an output schema exists. Nothing needed to invoke the tool correctly is missing.

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 only says 'Guide to read; defaults to guide', while the description explains exactly what each page contains (installation, conventions, catalog, imports, runnable assistant, parity, machine-readable surfaces). This goes far beyond the schema and gives the agent enough meaning to select the correct enum value confidently.

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?

States a clear action ('Read' / 'Select') and the resource (the Vlak guide), enumerating all five selectable pages. The purpose is unmistakable and the page options are explicitly listed, so no ambiguity remains about what the tool retrieves.

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 directs the agent to 'Read the general guide first' and maps each enum value to its intended content, which is strong guidance for parameter selection. It does not explicitly name sibling tools or state when get_guide is preferable to get_component or get_install, so the when-not guidance is incomplete.

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 readOnly and idempotent behavior, so no contradiction. The description adds that it returns three installation methods plus CSS-only markup and registry dependencies, which is useful. However, it does not clarify whether installation is actually performed or just documented, and it omits details like error behavior or output format.

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?

One sentence packs in the key output details: three install methodsasi and CSS-only markup. It is concise and front-loaded, though the parenthetical list is slightly awkward and makes the sentence dense.

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 simple read-only tool with one parameter retriever, the description conveys the main output content (install methods and CSS-only markup with registry dependencies). It does not explicitly mention the return format or errors, but the read-only annotations and simple input reduce the need for more.

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 covers the single parameter 'name' with 'Component name', and the description references 'one component' without adding further semantics. The parameter meaning is fully documented by the schema, so the description provides little additional 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 title 'Get install commands' and description clearly identify the resource: install instructions for a component. It enumerates the three install methods and CSS-only markup, which distinguishes it from siblings like get_component or get_tokens. However, the description is a noun phrase without an explicit verb like 'Returns' or 'Gets,' so it is clear but not maximally 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 implies this tool is for retrieving install commands for a single component, but it does not explicitly state when to use it over alternatives like get_component or list_components. There is no direct comparison or exclusion guidance for sibling 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context by specifying exactly what data is exposed (light and dark values, StyleX aliases, raw groups), which helps set expectations beyond the empty parameter schema.

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

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

A single dense sentence front-loads the main subject and then lists the included categories with no filler. Every phrase adds information about what the tool returns.

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 zero parameters, a rich output schema, and comprehensive annotations, the description fully covers the invocation context. An agent knows exactly what this tool provides and can safely call it without additional guidance.

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, so the description need not explain parameter meaning. The baseline of 4 applies; the description still adds value by clarifying the scope of the returned token data.

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 specific resource (design tokens page) and enumerates its contents: CSS custom properties, light/dark values, StyleX aliases, and raw token groups. This clearly distinguishes it from sibling get_* tools like get_component and get_guide without needing to inspect schemas.

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 intended use is implied: call this when design tokens or CSS custom properties are needed. However, there is no explicit when-to-use guidance or mention of alternatives, even though similar siblings exist. The description makes the use case obvious but leaves routing to inference.

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 a read-only, idempotent operation, so the description does not need to restate safety. It adds no behavior beyond returning all matching components and does not mention pagination, ordering, or response size, but the low complexity and readOnlyHint make these gaps less critical.

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 compact, front-loaded with the tool's core purpose, and adds the essential filter vocabulary without extraneous detail. Every sentence contributes useful 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 low-complexity read-only listing tool with one optional parameter and no output schema, the description gives enough to call it correctly: it specifies what is returned)Skip? it specifies returned fieldsLEY and enumerates valid categories. It does not discuss pagination or result limits, but the nature of the operation makes that a minor gap.

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 already describes category as 'Only this category', and the description adds the complete set of valid category values, which is genuinely useful since the schema has no enum list. This goes beyond what the schema alone provides, though not by a large margin.

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'), identifies the resource ('components in the catalogue'), and names the returned fields (name, title, description, category, aliases). It does not explicitly contrast against sibling tools like get_component or search_components, but 'Every component' plus 'Filter by category' conveys the list-all scope clearly.

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 explains the optional category filter and lists valid category values, which gives clear usage context. However, it does not explicitly say when to prefer this tool over list_component or search_components, so an agent gets no direct sibling differentiation.

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

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

Annotations already provide readOnly, idempotent, and non-destructive hints, so the behavior bar is lower. The description adds valuable normalization behavior: spacing, punctuation, and capitalization don't affect matching. It also discloses that results are ranked by field, which is beyond what the annotations or schema state.

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 the core purpose, and follows with matching details and ranking behavior. The alias list adds specificity without bloat and every clause 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?

Given the small parameter surface, rich input-schema, output schema, and annotations, the description adequately completes the picture. It addresses the non-obvious matching semantics, though it doesn't mention potential null results or pagination behavior—minor gaps not essential to selecting the 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?

While the schema already covers 100% of the parameter, the description dramatically expands term semantics by revealing it can match aliases like shadcn/ui or common names, plus rs-* classes. This goes beyond the schema's simple examples and meaningfully improves correct invocation.

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 opens with a specific verb and resource, 'Find components', and goes on to enumerate the searchable fields (name, title, description, alias, rs-* class). This clearly differentiates it from sibling tools like get_component (exact fetch) or list_components (enumeration).

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 gives a clear sense of what kinds of terms can be searched (fuzzy/alias/rs-*), so it implies usage for locating components without exact IDs. However, it never explicitly names when to prefer this over get_component or list_components, leaving alternative selection to inference.

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