Get the Design for Delight (D4D) framework templates. Returns customer problem statement, ideal state, hypothesis, LOFA, and experiment templates for structured product thinking.
| Name | Required | Description | Default |
|---|---|---|---|
| stage | No | Which stage of the D4D loop to return. Default: full (all stages) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With annotations already declaring readOnlyHint=true, idempotentHint=true, and destructiveHint=false, the safety profile is well-covered. The description adds that the tool 'Returns customer problem statement, ideal state, hypothesis, LOFA, and experiment templates,' which explains the composition of the response beyond what annotations state. However, no additional behavioral constraints like rate limits, authentication requirements, or response format details are disclosed.
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, front-loaded with the core action and audience ('Design for Delight (D4D) framework templates') followed by specifics about what's returned. 'structured product thinking' adds some flavor but is somewhat generic. Overall, efficient and focused, with minimal fluff.
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 this is a simple read-only tool with a single optional parameter (100% schema coverage) and a safe annotation profile (read-only, idempotent, non-destructive), the description is mostly complete. It identifies what's returned. Someminor gaps exist: no examples of usage, and no explicit statement that it returns all templates (vs. a subset) without filters, which would help round out the 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?
Schema description coverage is 100%, including descriptive text for the 'stage' parameter and its enum values. The description's mention of templates is generic and doesn't substantially add to the schema's own documentation. Baseline 3 is appropriate here since the schema handles the documentation burden.
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 gets D4D framework templates: 'customer problem statement, ideal state, hypothesis, LOFA, and experiment templates.' It uses a specific verb ('Get') and resource ('D4D framework templates'). While siblings like get_design_system and get_content_pattern exist, the description's focus on D4D distinguishes it, though it could be stronger in explicitly contrasting with these similar tools.
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 says it's 'for structured product thinking,' implying a context of use but provides no explicit when/when-not guidance or alternatives. Context signals show 49 sibling tools like get_design_system and get_content_pattern, suggesting related frameworks exist, but the description doesn't help an agent decide between them. The usage guidance is implicit ('structured product thinking') rather than explicit.
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.
