AI Canvas

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

Annotations already include readOnlyHint, openWorldHint, and idempotentHint. The description adds valuable context by stating the return includes metadata and the entire source code as a string, and emphasizes that 'The source is the exact file the CLI install would write into a project.' This goes beyond the annotations and clarifies the output nature.

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 with the primary action front-loaded. The second sentence provides usage context and the third adds a clarifying detail about source exactness. Every sentence serves a purpose 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?

For a simple one-parameter tool with no output schema, the description sufficiently explains what is returned (metadata + source code string), when to use it (after selection or before install), and the meaningful detail that it is the exact file the CLI would write. This is complete for the tool's 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% with a descriptive slug parameter that includes examples and instruction to use exact slugs from list/search results. The tool description adds no additional parameter-specific information beyond what the schema already provides, 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?

The description clearly states the tool returns 'the complete record for a single component: metadata + the entire .tsx source code as a string.' It differentiates from list/search tools by specifying 'a single component' and 'Use after the user picks one from a search/list result.' This is a specific verb+resource+output with clear differentiation.

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

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

Explicit usage guidance is provided: 'Use after the user picks one from a search/list result and wants to see the implementation, or before installing if they want to inspect first.' This tells the agent exactly when to invoke this tool and implies the alternative (using search/list) for finding components.

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 read-only and idempotent hints. The description adds valuable context by mentioning the prerequisite setup and the recommended post-call action. However, it does not detail the exact return format (e.g., plain string vs. JSON), which would be helpful without an output schema. No contradiction with 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?

The description is extremely concise, using two sentences to convey purpose, usage instructions, prerequisites, and a fallback. It is front-loaded with the core function and 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?

For a tool with a single parameter, clear annotations, and no output schema, the description covers the essential aspects: what it does, when to use it, prerequisites, and what to do after calling. It is complete enough for an agent to invoke correctly without needing extra context.

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, with slug described and an example provided. The description adds minimal parameter-specific meaning beyond that, but it reinforces that the command is for a single component. This is baseline-appropriate since the schema already handles parameter semantics.

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

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

The description clearly states the tool returns a shadcn CLI command to install a single component, using a specific verb (return) and resource. It distinguishes itself from sibling tools like get_component by focusing on the install command rather than the component code 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?

Explicit guidance is provided: it requires shadcn-aware setup (Tailwind v4, components.json) and specifies a fallback to get_component for projects without that setup. It also instructs to suggest running the command or run it yourself if shell access is available, giving clear when-to-use and alternative 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?

Annotations already provide readOnly, openWorld, and idempotent hints. The description adds useful behavioral context by noting the response contains every file and shared tokens/utilities, which implies a comprehensive bulk retrieval. No contradictions and no missing critical behavior for a read-only 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 sentences, front-loaded with the action and scope, no repetition of schema details, and every sentence serves a purpose. Very concise and well structured.

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

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

For a single-parameter read-only tool with strong annotations, the description fully covers what it returns and when to use it. The absence of an output schema is acceptable since the description clarifies the content type (files, components, tokens).

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%, and the schema already describes the slug parameter with an example and a pointer to list_systems for discovery. The tool description adds no additional parameter context, matching the baseline expectation.

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 returns all files for a design system, including components, tokens, and utilities. It distinguishes itself from sibling tools that retrieve individual components (get_component) or templates (get_template) by emphasizing the whole-system scope.

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 says to use this tool when the user wants to adopt a whole system rather than pick individual components. It clearly implies the alternative use case for component-level retrieval, though it does not name the specific sibling 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?

Annotations already indicate read-only, open-world, and idempotent behavior. The description adds valuable context that it aggregates all files (composition + components + tokens) and mentions a CLI command for installation. This complements the annotations without 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?

The description is two sentences long, front-loaded with the core action and resource, and every clause adds meaningful information (scope, usage examples, installation hint). There is no filler or unnecessary repetition.

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 single-parameter tool with good annotations, the description explains what it does and when to use it well. However, since there is no output schema, it would benefit from stating the return format (e.g., files as URLs or inline content), which is 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?

Schema coverage is 100% and the slug parameter is already described with examples and a pointer to list_templates. The description does not add additional parameter meaning beyond re-stating that a 'single template' is fetched, which is already present in 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 leads with 'Return all files for a single template' and specifies that it includes 'the example composition plus every component it uses plus shared tokens'. This clearly articulates the verb, resource, and scope, distinguishing it from sibling tools like get_component or list_templates.

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?

It explicitly states user intents: 'Use for "I want exactly this dashboard" / "give me the entire mission-control screen"'. This provides clear context for when to use the tool, though it does not explicitly mention when not to use it or name 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 annotations already declaring readOnlyHint, openWorldHint, and idempotentHint, the safety profile is covered. The description adds useful behavioral detail: it returns every category and includes component counts, which goes beyond what annotations provide. No contradiction with 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?

The description is concise and front-loaded, with two sentences: the first states exactly what the tool returns, the second gives a practical usage example. No redundant 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 simple, parameterless tool with no output schema, the description sufficiently explains the return value (categories with counts) and provides usage context. It is complete enough for an AI agent to understand the tool's purpose and when to use it, especially given the rich annotations.

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

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

The tool has zero parameters, so there is nothing for the description to explain. Baseline of 4 is appropriate for a parameterless tool; the description correctly focuses on the return value and usage rather than parameter details.

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

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

The description uses a specific verb ('Return') and resource ('every category in the AI Canvas standalone component library'), and specifies the output includes the number of components per category. This clearly distinguishes it from sibling tools like list_components or search_components, which operate on components rather than categories.

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 advises using this tool to 'orient before listing or searching,' which gives clear context and a specific use case. It does not name alternative sibling tools directly, but the guidance is unambiguous and helpful for an AI agent deciding when to invoke 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?

Annotations already provide readOnlyHint, openWorldHint, and idempotentHint. The description adds useful behavioral context by listing the exact fields included in results and disclosing pagination behavior via `limit` and `offset`. No contradictions with 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?

Three concise sentences, front-loaded with the core purpose, followed by usage guidance and return-value details. 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 no output schema, the description compensates by explaining what each result contains. It covers usage context, filtering, and pagination, making the tool fully understandable and appropriately complete for its simplicity.

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 fully documents all three parameters. The description reinforces the optional filter and pagination but adds little beyond the schema's own 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 action ('Return AI Canvas components') and the optional filter by category. It distinguishes from siblings by indicating this is a listing tool and even references `list_categories` and `get_component`, making the tool's scope obvious.

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 when to use: 'Use after `list_categories` to drill into one, or pass no filter to browse everything.' It also tells the agent it can avoid a separate `get_component` call because results include enough fields, effectively guiding tool selection.

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

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

The annotations already declare readOnlyHint, openWorldHint, and idempotentHint, covering safety and behavior. The description adds conceptual context (that design systems are installable in one CLI command) but does not disclose additional behavioral traits such as pagination, ordering, or output format. This is adequate but not rich, so a 3 is appropriate.

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, with the core purpose front-loaded in the first sentence ('Return every design system available on AI Canvas'). The second sentence adds a definition and usage guidance without waste. 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?

Given the tool's simplicity (0 params, no output schema, strong annotations), the description is nearly complete. It states what the tool returns and when to use it. It could mention what fields the output includes (e.g., IDs for use with get_system), but this is not necessary for a simple list tool, so a 4 is justified.

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, and the baseline for 0 params is 4. The description adds conceptual clarity about design systems but does not need to explain parameter details since there are none. The description's definition of a design system adds some semantic value beyond the empty 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 returns every design system available on AI Canvas, with a specific verb ('Return') and resource ('design systems'). It also defines what a design system is, distinguishing it from sibling tools like get_system (which retrieves a specific system) and list_components (which lists single components).

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 usage context: 'Use when the user asks about themes, design systems, or wants more than a single component.' It clearly implies when to use this tool over alternatives but does not formally name exclusions or alternative tools, making it a 4 rather than a 5.

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, openWorldHint, and idempotentHint, covering safety and mutability. The description adds contextual detail about templates being installable via CLI and being complete screens, but does not disclose further behavioral aspects like pagination, sorting, or response structure. Since annotations carry the safety profile, this adds some value but not rich 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?

Three sentences: state action, define key term, give usage guidance. Zero fluff, front-loaded with the core purpose, and every sentence earns its place. Highly concise while retaining all essential 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 simple list tool with one optional parameter, strong annotations, and no output schema, the description covers purpose, definition, and usage. It does not describe the return format, but that is not required given the lack of an output schema and the simplicity of the operation. The only minor gap is not explicitly mentioning the 'system' filter in prose, but the schema covers it, so the description 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 description coverage is 100% for the single optional 'system' parameter, which already explains its purpose and example value. The description does not mention the parameter, but the schema fully documents it, so the baseline 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 opens with a specific verb+resource: 'Return every ready-made template on AI Canvas' and further defines what a template is (complete example screen built from one design system). It explicitly distinguishes itself from get_template ('before fetching one with get_template') and from single-component tools, making its purpose unmistakable.

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 explicit when-to-use guidance: 'Use to discover what full screens exist before fetching one with get_template' and 'when the user asks for "a dashboard" / "an admin screen" rather than a single component.' It also names get_template as the alternative to use after discovery, providing a clear decision path.

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?

Beyond the read-only/open-world/idempotent annotations, the description discloses fuzzy matching behavior, the specific fields searched, relevance ranking, and that results include screenshot URLs and install commands. This adds meaningful behavioral context without contradicting 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?

The description is two sentences: the first states the core function, and the second adds usage examples and result contents. Every sentence earns its place with no redundancy, and it is front-loaded with the essential action.

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 search tool with two parameters and no output schema, the description covers what is searched, how results are ordered, and what results contain (screenshots and install commands). Combined with schema and annotations, it provides enough context for an agent to select and invoke the tool 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?

The schema already fully documents both parameters (query and limit), so baseline is 3. The description adds value by specifying the fields searched (slug, name, description, categories, tags) and noting the fuzzy, relevance-ranked nature of the search, going beyond the schema's generic description.

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 is a fuzzy keyword search across component slug, name, description, categories, and tags, returning ranked matches. This distinguishes it from sibling tools like list_components (listing) and get_component (retrieval by ID).

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 says 'Use when the user describes what they want in their own words' with concrete examples, providing clear context for when to use the tool. It doesn't name alternatives, but the guidance is strong enough to differentiate it from listing or retrieval tools.

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