Get content design patterns — copy recipes for error messages, empty-state copy, notifications, and form validation. Returns do's, don'ts, good/bad examples, evidence, and a checklist.
| Name | Required | Description | Default |
|---|---|---|---|
| type | Yes | Content pattern type |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds transparency about expected return content (do's/don'ts, examples, evidence, checklist), which is valuable since there is no output schema. No contradictions.
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, front-loaded with the verb 'Get', no fluff, and all information is relevant 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 simple read-only tool with one enum parameter, the description provides sufficient guidance: what it returns, the categories, and the content structure. The absence of an output schema makes the description's return-item listing essential and 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?
The single 'type' parameter is fully documented in the schema with an enum, and the description restates the categories in natural language. This adds minimal meaning beyond the schema, so the 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 tool's function: retrieving content design patterns for specific UX copy types. It lists the return contents (do's/don'ts, examples, evidence, checklist) and distinguishes it from sibling tools like get_content_principles or get_content_system.
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 by enumerating the four pattern types (error messages, empty-state, notifications, form validation), allowing an agent to infer when to use it. It does not explicitly mention alternative tools or exclusions, but the scope is well-defined.
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.
