Get a brand's content design system — voice attributes, tone shifts by context, vocabulary (use/avoid/never), grammar rules, content patterns for errors/empty-states/buttons/etc., and inclusive language guidance.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Content system ID (e.g. 'conversational-product-voice', 'gov-uk', 'shopify-polaris', 'atlassian') | |
| section | No | Return just one section. Default: all. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and non-destructive, so no need to repeat safety. The description adds the scope of content returned, which is useful. However, it does not disclose behavior like whether 'all' is the default, how sections are returned, or if the response includes examples, but this is minor given 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 a single, dense sentence that lists all major content components without fluff. It is front-loaded with the action and resource ('Get a brand's content design system') and efficiently enumerates the sections. No redundant or 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?
With 100% schema coverage and a simple 2-parameter tool, the description is quite complete. It covers what the tool returns. However, it does not mention that 'section' defaults to 'all' (though the schema does), and there is no output schema, so agents might not know the exact structure. Given the tool's moderate complexity and good schema, this is adequate.
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 all parameters with detailed descriptions (id example, section enum with explicit values and default). The description adds meaning by explaining the content of the system, but it doesn't elaborate on the section parameter (e.g., what each section includes), which the schema already describes. Given 100% schema coverage, baseline 3, and the description adds some context about the tool's purpose, a 4 is justified.
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 a brand's content design system, listing specific components (voice attributes, tone shifts, vocabulary, grammar rules, content patterns, inclusive language). This distinguishes it from siblings like get_content_pattern (which likely returns a single pattern) and get_design_system (which likely covers visual design).
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 retrieving brand content guidelines, and the list of sections suggests when to use section filters. However, it does not explicitly state when to use this over get_content_principles or get_brand_system, which might overlap. No direct exclusions or alternatives are named.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clearly distinct purposes (audit_* vs get_* vs list_* vs generate_* vs score_*), but there is notable overlap among audit_page, audit_layout, score_page, and audit_url (all audit rendered HTML, with audit_page and score_page explicitly sharing checks; audit_screen and audit_ios_screen are aliases). The get_* family (get_pattern vs get_content_pattern vs get_service_pattern, get_principles vs get_brand_principles vs get_content_principles) have overlapping boundaries that may cause misselection.
Names follow a consistent verb_noun pattern (audit_*, get_*, list_*, generate_*, score_*, compose_*, suggest_*, search_*), which is predictable and readable. Minor deviations exist: 'evaluate_design' uses evaluate_ instead of audit_/score_, and 'process' isn't present but 'compose_system' uses compose_ instead of generate_/get_. Overall the convention is strong and consistent.
45 tools is far beyond the typical well-scoped server (3-15 tools) and even beyond the 'heavy' 25+ threshold. The server appears to be an all-in-one design/UX knowledge base and auditing suite, but the sheer count makes discovery and selection overwhelming, and many tools (e.g., multiple audit_* variants for mobile platforms) could be consolidated.
The server covers a wide domain: audits for web/mobile/RN/SwiftUI, design tokens, UX principles, content systems, business strategy, creative scoring, and service design. However, there are gaps: no tool for creating or editing design systems (only get/generate), no update/delete operations anywhere (all read-only or audit-only), and the creative side has list/score but no generation tool. The set feels broad but shallow in lifecycle coverage.
