phonebook
This MCP server lets you set up, diagnose, and generate a static preview gallery for a mobile app without manual CLI work.
Analyze coverage: scans the codebase to find UI components and which previews/states/themes are missing
Check setup: verifies the project is correctly configured for preview generation (dependencies, test target, toolchain)
Get preview guidance: returns naming conventions and ready-to-paste preview code templates for a component
Run generate: renders all previews into a bundle (manifest + images) for Android or iOS
Run build: builds the static HTML gallery site from a generated bundle, optionally into a separate output directory
Analyzes Android projects that use Compose @Preview, checks setup compatibility, assesses preview coverage, and generates a static HTML gallery of component screenshots from the existing previews.
Runs on iOS projects with SwiftUI #Preview, checks setup, identifies missing previews, and generates a static HTML gallery of component screenshots from the existing previews.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@phonebookRun doctor and generate the gallery for this repo"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Phonebook turns screenshots your team already has into a Storybook-style component gallery. No new test code, no design tokens to maintain by hand — it renders what's already in your codebase into a static site designers can open without installing anything. Each repo runs Phonebook independently; v1 is single-platform, so one Android repo (or one iOS repo) produces one bundle and one site.
Features
Zero new test code — reuses
@Preview/#Previewyou've already writtenNo SaaS account — self-hosted, runs entirely in your CI or locally
MCP-first — a coding agent can check setup, analyze coverage, add missing previews, and build the gallery for you
Smart component grouping —
component / statecards inferred from preview names, no required annotationCross-platform — Android (Roborazzi + ComposablePreviewScanner, runs on the JVM, no emulator) and iOS (SnapshotPreviews, runs on a simulator)
Version-aware setup —
init/doctorresolve library versions against your project's Kotlin version and catch Kotlin/Roborazzi metadata mismatches before they cause opaque compiler crashes
Related MCP server: Pixl
Demo


A gallery generated from samples/ios — component / state cards grouped from the app's own #Previews, no extra annotation.
How it works
phonebook generateruns your platform's preview-rendering engine and harvests the output into a bundle (manifest.json+images/).Android: Roborazzi + ComposablePreviewScanner, run on the JVM via Robolectric. No emulator, works on Linux CI.
iOS: SnapshotPreviews, run via
xcodebuild teston a simulator. Requires macOS.
phonebook buildturns that bundle into a static site — by default it writesindex.htmldirectly into the bundle directory (reusing the images already there, no copying), so the site lands at<bundle>/index.html. Pass-o <dir>to instead copy everything into a standalone site directory (for publishing elsewhere, or later merging multiple bundles). Plain HTML/CSS/JS, works fromfile://or any static host.
Installation
npm install -g @stag-build/phonebookbrew install stag-build/phonebook/phonebookOr tap first, then install:
brew tap stag-build/phonebook
brew install phonebookFormula source: stag-build/homebrew-phonebook.
npx @stag-build/phonebook <cmd>Using it with a coding agent (recommended)
Most people won't run the CLI directly — Phonebook is built to be driven by a coding agent (Claude Code, Codex, etc.) through its MCP server. The agent adds previews, runs setup checks, and generates the gallery for you; the CLI underneath is the engine it calls.
The server runs via npx @stag-build/phonebook mcp — no install step needed. Pick your client below.
claude mcp add phonebook -- npx -y @stag-build/phonebook mcpAdd to ~/.codex/config.toml:
[mcp_servers.phonebook]
command = "npx"
args = ["-y", "@stag-build/phonebook", "mcp"]Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):
{
"mcpServers": {
"phonebook": {
"command": "npx",
"args": ["-y", "@stag-build/phonebook", "mcp"]
}
}
}Add to .cursor/mcp.json (project) or ~/.cursor/mcp.json (global):
{
"mcpServers": {
"phonebook": {
"command": "npx",
"args": ["-y", "@stag-build/phonebook", "mcp"]
}
}
}Add to .codex/config.toml at your project's workspace root. Xcode's agent runs with a minimal PATH, so the command wraps npx in a shell that adds the usual Homebrew/nvm locations first:
[mcp_servers.phonebook]
command = "/bin/zsh"
args = [
"-lc",
"PATH=/opt/homebrew/bin:/usr/local/bin:/usr/bin:/bin:/usr/sbin:/sbin; npx -y @stag-build/phonebook mcp"
]
enabled = trueAdd the mcpServers block to ~/Library/Developer/Xcode/CodingAssistant/ClaudeAgentConfig/.claude.json:
{
"mcpServers": {
"phonebook": {
"command": "/bin/zsh",
"args": [
"-lc",
"PATH=/opt/homebrew/bin:/usr/local/bin:/usr/bin:/bin:/usr/sbin:/sbin; npx -y @stag-build/phonebook mcp"
]
}
}
}Android Studio (Gemini Agent Mode): not supported yet — its MCP integration only connects to remote httpUrl servers, not local stdio processes like Phonebook's. Use one of the terminal-based clients above (Claude Code, Codex CLI) from the Android repo instead.
Then, from a chat in your Android or iOS repo, just ask:
"Use the phonebook MCP and create a catalog for my designer."
The agent figures out the rest — checking setup, filling in missing previews, generating, and building the site. For more targeted asks, it also exposes: check_setup (setup diagnosis, same as phonebook doctor), analyze_coverage (components missing previews or dark variants), get_preview_guidance, run_generate, and run_build.
Quickstart: Android
Run phonebook init first — it detects your project's Kotlin version and prints these instructions with library versions resolved to be compatible with it (e.g. Kotlin 2.0 projects get Roborazzi 1.60.0; Kotlin 2.2+ gets the latest). The versions below are what a current-Kotlin project gets (see samples/android/app/build.gradle.kts for a full working example):
// app/build.gradle.kts
plugins {
id("io.github.takahirom.roborazzi") // root build.gradle.kts: version "1.72.0" apply false
}
roborazzi {
generateComposePreviewRobolectricTests {
enable = true
packages = listOf("dev.stag.phonebook.sample") // your app's package
}
}
dependencies {
testImplementation("org.robolectric:robolectric:4.14.1")
testImplementation("io.github.takahirom.roborazzi:roborazzi:1.72.0")
testImplementation("io.github.takahirom.roborazzi:roborazzi-compose:1.72.0")
testImplementation("io.github.sergio-sastre.ComposablePreviewScanner:android:0.9.3")
testImplementation("io.github.takahirom.roborazzi:roborazzi-compose-preview-scanner-support:1.72.0")
testImplementation("androidx.compose.ui:ui-test-junit4") // version from your Compose BOM, or pin one
}Add a phonebook.config.json next to settings.gradle.kts:
{
"appName": "My Android App",
"platform": "android",
"android": { "modules": [":app"], "variant": "debug" }
}Then, from the repo containing Phonebook:
npx @stag-build/phonebook generate -C /path/to/your/android/repo
npx @stag-build/phonebook build -C /path/to/your/android/repoOpen phonebook-out/index.html.
Quickstart: iOS
Add the SnapshotPreviews SPM package to your project and a small XCTest target that subclasses SnapshotTest (see samples/ios for a full working example):
// PhonebookSnapshotTests.swift
import Foundation
import SnapshottingTests
final class PhonebookSnapshotTests: SnapshotTest {
override class func snapshotPreviews() -> [String]? {
guard let raw = ProcessInfo.processInfo.environment["SNAPSHOTS_ONLY_FILTER"], !raw.isEmpty else {
return nil // record every #Preview
}
return raw.components(separatedBy: "\n")
}
}Reading SNAPSHOTS_ONLY_FILTER is what lets phonebook generate --changed (or --files A.swift,B.swift)
render only the previews in the files you just edited; with the variable unset, every #Preview is recorded
as before.
Add phonebook.config.json next to your .xcodeproj:
{
"appName": "My iOS App",
"platform": "ios",
"ios": {
"project": "MyApp.xcodeproj",
"scheme": "MyApp",
"simulator": "iPhone 17 Pro"
}
}Your scheme must build and test the snapshot test target (see PhonebookSample.xcscheme in the sample). Then:
npx @stag-build/phonebook generate -C /path/to/your/ios/repo
npx @stag-build/phonebook build -C /path/to/your/ios/repoOpen phonebook-out/index.html.
Naming convention
Phonebook groups screenshots into component / state cards from your existing preview names — no required annotation. See docs/naming-convention.md for the full rules and examples.
Configuration
phonebook.config.json:
Key | Type | Default | Notes |
| string | — | Required. Shown in the gallery header. |
|
| — | Required. |
| string |
| Bundle output directory, relative to the config file. |
| string[] |
| Gradle modules to record. |
| string |
| Build variant; Phonebook runs |
| string | — | Path to |
| string | — | Path to |
| string | — | Required. Scheme that includes the SnapshotPreviews test target. |
| string |
| Simulator device name used for |
| string | auto-detected |
|
Both generate and build accept -C <dir> (project directory containing phonebook.config.json). generate takes -o <dir> to override the bundle output and --allow-empty to tolerate a run that records no previews. build takes an optional bundle path — with none, it uses the project's bundle directory — and -o <dir> for the site output; without -o, build writes index.html straight into the bundle directory and reuses its images/ in place (no copying), which is what the quickstarts above do. Pass -o <dir> to instead copy the bundle's images into a separate, standalone site directory.
phonebook init and phonebook doctor
phonebook init detects your platform and scaffolds phonebook.config.json plus the dependency/setup snippets — with library versions resolved against your project's Kotlin version and your app package filled in. It never edits your build files for you.
phonebook doctor checks that everything generate needs is wired up: plugin and test dependencies (resolved through Gradle version catalogs when you use them), the scanner's packages value, Kotlin/Roborazzi compatibility, and the toolchain (JDK/Xcode/simulator). Add --deep to also compile the test sources — slower, but authoritative when a static check and reality disagree. On iOS, if SnapshotPreviews is linked but no SnapshotTest subclass exists yet, doctor names the exact target and folder to add it to (parsed from the .pbxproj), so you're never just told to "add the class" with no location.
phonebook init --write-snapshot-class is the one exception to init's hands-off rule: when doctor's iOS check identifies the linking target and that target's source folder is one of Xcode's filesystem-synchronized groups, it writes <folder>/PhonebookSnapshots.swift directly — safe because a synchronized folder is picked up by Xcode automatically, so no project.pbxproj edit is made. It refuses (with the reason) in every other case: no SnapshotPreviews wiring yet, a non-synchronized-group project, or a subclass that already exists.
phonebook mcp runs the MCP server — see "Using it with a coding agent" above for setup and example prompts.
Requirements
Android: JDK 17+. No emulator needed — Roborazzi renders on the JVM via Robolectric, so generate runs on Linux CI.
iOS: macOS with Xcode installed, plus a booted or bootable simulator (generate runs xcodebuild test against a named simulator destination). Requires a macOS runner in CI.
See docs/ci.md for CI recipes and docs/naming-convention.md for the naming rules.
Roadmap
Post-v1 (M5), not yet built:
Search and filters in the generated gallery
Multi-bundle merge with a side-by-side view (cross-platform sites)
Version diffing between two runs (the manifest already carries commit + image hashes to enable this)
Additional CI recipe docs
License
MIT — see LICENSE.
Available Tools
5 toolsanalyze_coverageA
Scan the codebase for UI components and the previews that cover them, and report what each component is missing: states implied by its parameters, environment objects no preview supplies, dark theme, large text, and localization when the project ships one. Read-only — it reports the gaps, it does not write previews. After editing, pass changed: true (or paths) to hear only about what you touched; the whole project is always scanned either way, so the answers stay correct.
| Name | Required | Description | Default |
|---|---|---|---|
| dir | No | Project directory containing phonebook.config.json | . |
| paths | No | Report only components declared in these files or directories, relative to the project directory. The whole project is still scanned. | |
| changed | No | Report only components in files with uncommitted git changes — what you just edited. Ignored outside a git repository. Combined with paths when both are given. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It explicitly states 'Read-only — it reports the gaps, it does not write previews,' and clarifies that the entire project is scanned even when filters are applied, ensuring the answers stay correct. This is honest and thorough for a read-only tool.
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 paragraph but each sentence serves a purpose: purpose, behavioral safety, and usage guidance. It is front-loaded with the main objective, and the filtering instructions are kept concise. Slightly long but no waste.
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 read-only tool with 3 optional parameters and no output schema, the description adequately explains what is analyzed, what is reported, and how filters behave. It does not specify the exact return format, but that is a minor gap given the clarity of the rest. The tool is fully callable correctly from the description alone.
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 baseline is 3. The description adds valuable context beyond the schema: it explains that paths restrict reporting but not scanning, that changed is ignored outside a git repo, and that both combine when given together. This clarifies edge cases and adds meaning to each parameter.
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 verb ('Scan'), resource ('codebase'), and a precise objective: report what each UI component is missing in preview coverage (states, environment objects, dark theme, large text, localization). It clearly distinguishes itself from sibling tools like get_preview_guidance (which presumably offers guidance) and run_build/run_generate/check_setup (which are operational, not analytical).
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 when to use the changed and paths parameters ('After editing, pass changed: true (or paths) to hear only about what you touched'), and notes that the whole project is always scanned regardless. It doesn't explicitly contrast with get_preview_guidance, but the analytical vs. guidance nature is implied, and the filtering usage is well covered.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
check_setupA
Check that the project is correctly set up for phonebook generate (same checks as phonebook doctor): libraries wired, test target present, JDK/Xcode/simulator available.
| Name | Required | Description | Default |
|---|---|---|---|
| dir | No | Project directory containing phonebook.config.json | . |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does communicate that the tool performs checks, names the checks (libraries, test target, JDK/Xcode/simulator), and implies a read-only nature. However, it does not describe failure behavior, exit codes, output format, or whether the tool attempts any fixes, leaving some behavioral ambiguity.
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 one tight, well-structured sentence. It front-loads the purpose, gives the equivalence to `phonebook doctor`, and lists the main checks without 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?
For a simple single-parameter check tool with no output schema and no annotations, the description covers the essential context: what is checked and why. It could be slightly more complete by stating what a successful or failed check returns, but the current level is adequate for selection and invocation.
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 parameter `dir` is fully documented in the schema with a clear description and default value, so schema coverage is 100%. The tool description adds no additional parameter semantics, which is acceptable given the schema already handles it.
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 ('Check') and resource ('project setup'), names the exact command it supports (`phonebook generate`), and enumerates the concrete checks performed. It clearly distinguishes itself from generation/build tools like `run_generate` and `run_build`.
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 clearly implies this is a precondition check for `phonebook generate`, and the reference to `phonebook doctor` provides an equivalence that helps the agent understand behavior. It does not explicitly state 'use before run_generate' or list when not to use it, so it falls just short of full explicit routing guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_preview_guidanceA
Return the preview naming convention plus a ready-to-paste preview code template for a component, so any agent writes consistent previews.
| Name | Required | Description | Default |
|---|---|---|---|
| states | No | State names, e.g. ["Enabled", "Disabled"] | |
| platform | Yes | Target platform | |
| component | No | Component name, e.g. "Button" |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the behavioral disclosure burden. It clearly discloses a non-mutating return ('Return') and the output type (naming convention + template), but it does not describe output format, failure behavior, or prerequisites. For a simple getter this is adequate but incomplete.
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 with no filler, and the key deliverable is front-loaded before the outcome clause. Every phrase 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?
Only three parameters with 100% schema coverage reduce the burden on the description. However, with no annotations and no output schema, the description could usefully state how platform, states, and component are handled, or when this guidance should be fetched relative to run_generate and run_build. These are gaps but not fatal ones.
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%, so the baseline for this dimension is 3. The description's 'for a component' aligns with the 'component' parameter but adds no value beyond the schema's documented parameters; nothing is said about how states or platform affect the returned template.
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 identifies a concrete resource ('preview naming convention plus a ready-to-paste preview code template'), with the desired outcome 'so any agent writes consistent previews.' It clearly separates itself from execution-oriented siblings like run_generate and run_build, though it does not explicitly name them.
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 phrase 'so any agent writes consistent previews' implies this tool should be consulted before writing a preview, but there is no explicit statement of when to use it versus siblings or when not to use it. It lists no alternatives and no exclusion conditions, leaving agents to infer its place in the workflow.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
run_buildA
Build the static gallery site from a bundle, same as phonebook build <bundle>.
| Name | Required | Description | Default |
|---|---|---|---|
| bundle | Yes | Bundle directory produced by run_generate / `phonebook generate` | |
| output | No | Site output directory (default: the bundle directory itself, reusing its images) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description only states the core action; it does not disclose side effects such as writing into the bundle directory, overwriting output, or requirements. The output default noted in the schema is useful but outside the description, so the description itself carries too little behavioral burden.
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 front-loaded sentence with no filler, and the CLI-equivalent note is a compact way to anchor expected behavior. It is appropriately sized for a simple build command.
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 two-parameter tool with full schema coverage, the inputs are adequately specified, but the missing usage routing and side-effect disclosure leave the agent to infer workflow and safety. With no annotations or output schema, a bit more context would make it 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%, with both 'bundle' and 'output' documented, so the baseline applies. The description adds no parameter semantics beyond the schema other than echoing 'bundle'.
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?
Description opens with an explicit verb ('Build') and resource ('static gallery site'), and identifies the input ('from a bundle'), which clearly separates it from the sibling generation/analysis/check tools. The CLI alias reinforces the exact operation without ambiguity.
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 'from a bundle' wording and the schema's 'produced by run_generate' hint imply a build-after-generate workflow, but the description does not explicitly say when to use this tool versus siblings like run_generate or analyze_coverage. No when-not or alternative conditions are stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
run_generateA
Run the platform engine to render all previews and produce a bundle (manifest + images), same as phonebook generate. When previews crash, keeps what rendered and says which previews did not, by file and line. Pass changed or files to render only the previews declared in those files, for a faster loop while iterating.
| Name | Required | Description | Default |
|---|---|---|---|
| dir | No | Project directory containing phonebook.config.json | . |
| files | No | Render only previews declared in these files, relative to the project directory. | |
| changed | No | Render only previews declared in files with uncommitted git changes. Ignored outside a git repository. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must carry the full burden of behavioral disclosure. It does disclose crash handling (keeps what rendered, reports failures by file and line) and the bundle output, which is valuable. However, it omits other behavioral traits like side effects, idempotency, and prerequisites, leaving significant gaps.
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 no filler. It front-loads the core purpose, then adds crash behavior and usage tips. Every sentence earns its place and the structure is clean.
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 no output schema and no annotations, so the description should clarify return values and side effects. It mentions the bundle output but does not specify what the tool returns to the agent. It also does not state prerequisites like the config file beyond the dir parameter, leaving completeness gaps.
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?
All three parameters are fully described in the schema (100% coverage), so the description adds little beyond what is already structured. It mentions passing files or changed but does not elaborate on their semantics beyond the schema's 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 tool runs the platform engine to render previews and produce a bundle, referencing 'phonebook generate' for familiarity. It does not explicitly distinguish from sibling tools like run_build, but the core action is unambiguous and specific.
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 provides clear guidance on using the files and changed parameters to limit rendering to specific files for a faster iteration loop. However, it does not mention when to use this tool over siblings or any exclusions, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
2 tool updates
v0.1.7- Changed
analyze_coverage2 fields changed- added
Input schema / properties / changedAdded value: +{ + "description": "Report only components in files with uncommitted git changes — what you just edited. Ignored outside a git repository. Combined with paths when both are given.", + "type": "boolean" +} - added
Input schema / properties / pathsAdded value: +{ + "description": "Report only components declared in these files or directories, relative to the project directory. The whole project is still scanned.", + "items": { + "type": "string" + }, + "type": "array" +}
- Changed
run_generate2 fields changed- added
Input schema / properties / changedAdded value: +{ + "description": "Render only previews declared in files with uncommitted git changes. Ignored outside a git repository.", + "type": "boolean" +} - added
Input schema / properties / filesAdded value: +{ + "description": "Render only previews declared in these files, relative to the project directory.", + "items": { + "type": "string" + }, + "type": "array" +}
5 tool updates
v0.1.2- First observed
analyze_coverage - First observed
check_setup - First observed
get_preview_guidance - First observed
run_build - First observed
run_generate
TDQS
Scored across 5 tools
Each tool addresses a distinct step in the preview workflow: guidance, setup, coverage analysis, generation, and building. The only adjacent pair (run_generate and run_build) is clearly separated by bundle output versus static-site output.
Most names are imperative snake_case with a clear object (get_preview_guidance, analyze_coverage, check_setup). run_build and run_generate are a minor deviation because they use a run_ prefix around CLI command names rather than a pure verb_noun structure.
Five tools is well-scoped for a focused preview-generation workflow. Each tool earns its place by covering a separate phase from setup to final build, with no redundant or padding tools.
The tool set covers the full core loop: check setup, get authoring guidance, analyze coverage gaps, generate previews, and build the gallery site. The absence of a write-preview tool is fine because editing code happens outside the server, and the guidance plus coverage tools support that workflow.
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- FlicenseNot gradedqualityBmaintenanceEnables AI coding agents to inspect a project's design tokens and component catalog, audit React/Tailwind files against seven static UI quality rules, score overall UI health, and apply auto-fixes that are re-audited and reverted on regression. It works both locally over filesystem access and remotely on raw TSX/JSX or CSS snippets passed directly as strings.-