This server lets you drive Phonebook from a coding agent to turn existing Android/iOS previews into a static component gallery.
check_setup — Run phonebook doctor checks: verify libraries, test targets, JDK/Xcode/simulator, and Kotlin/Roborazzi compatibility.
analyze_coverage — Scan the codebase to see which UI components have previews and which states/themes are missing.
get_preview_guidance — Get the naming convention plus a ready-to-paste preview template for a component and states.
run_generate — Render all previews using the platform engine and produce a bundle (manifest + images).
run_build — Build the static HTML gallery site from a bundle, optionally into a standalone 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 "Install 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., "@phonebook Run 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.
Zero new test code — reuses @Preview / #Preview you've already written
No 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 / state cards inferred from preview names, no required annotation
Cross-platform — Android (Roborazzi + ComposablePreviewScanner, runs on the JVM, no emulator) and iOS (SnapshotPreviews, runs on a simulator)
Version-aware setup — init/doctor resolve library versions against your project's Kotlin version and catch Kotlin/Roborazzi metadata mismatches before they cause opaque compiler crashes
Related MCP server: Storybook MCP
A gallery generated from samples/ios — component / state cards grouped from the app's own #Previews, no extra annotation.
phonebook generate runs 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 test on a simulator. Requires macOS.
phonebook build turns that bundle into a static site — by default it writes index.html directly 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 from file:// or any static host.
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>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.
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.
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 SnapshottingTests
final class PhonebookSnapshotTests: SnapshotTest {
override class func snapshotPreviews() -> [String]? {
return nil // record every #Preview
}
}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.
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.
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 doctorphonebook 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.
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.
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
MIT — see LICENSE.
Scan the codebase for UI components and the previews that cover them: which have previews, which states/themes are missing. Read-only.
| Name | Required | Description | Default |
|---|---|---|---|
| dir | No | Project directory containing phonebook.config.json | . |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure, and it explicitly says 'Read-only,' which is the most critical safety trait for this tool. It does not mention potential runtime cost, config lookup behavior, or output shape, but the read-only guarantee is clearly and directly stated.
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 tight sentences with no filler. The main action and expected results are front-loaded, and 'Read-only' is appended as a concise safety qualifier without disrupting flow.
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 low-complexity tool with one optional, fully documented parameter and no output schema, the description provides enough context: what is scanned, what categories are reported, and that it is read-only. It would be slightly stronger with an explicit workflow hint relative to the sibling tools, but nothing essential to invoking it correctly is missing.
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 has 100% coverage for the single dir parameter: it documents type, default, and the 'Project directory containing phonebook.config.json' expectation. The tool description adds no parameter detail, but because the schema already fully documents it, the baseline of 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 opens with a concrete verb and resource: 'Scan the codebase for UI components and the previews that cover them.' It also names specific outcomes—which components have previews and which states/themes are missing—so an agent can distinguish this analysis task from siblings like run_generate or check_setup.
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 read-only analysis intent is clear, and the wording implies it is useful for assessing preview coverage before generation. However, the description never explicitly says when to prefer analyze_coverage over get_preview_guidance or how it fits relative to run_generate/run_build, so usage guidance remains implicit rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 | . |
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.
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" |
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.
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) |
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 the platform engine to render all previews and produce a bundle (manifest + images), same as phonebook generate.
| Name | Required | Description | Default |
|---|---|---|---|
| dir | No | Project directory containing phonebook.config.json | . |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description carries the full burden of behavioral disclosure. It explains the action and output but does not mention side effects (e.g., writing files to disk), prerequisites (e.g., a valid config file), or what the tool returns. For a generation tool that likely mutates the project directory, this lack of detail is a significant gap.
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 sentence that front-loads the action and result, then adds the CLI reference. There is no fluff or redundancy. It could benefit from a second sentence about prerequisites, but it earns a high score for efficiency and clear focus.
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 no annotations and no output schema, the description must cover the essentials. It states what the tool does and references the CLI, but it omits return value details, potential side effects, and any environmental requirements. Since the tool is simple (one parameter), the definition is adequate but not fully complete for an agent that has never seen the CLI command.
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 fully documents the only parameter (`dir`) with a clear description and default value, so the schema does the heavy lifting. The tool description adds no additional context about the parameter, such as path validation or behavior when omitted. Baseline 3 is appropriate given the high schema coverage.
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 ('Run the platform engine') and the concrete outcome ('render all previews and produce a bundle (manifest + images)'). It also references the CLI equivalent (`phonebook generate`), which anchors its role. The intended output clearly differentiates it from siblings like run_build or analyze_coverage, even though no explicit comparison is made.
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 when you need to generate previews and a bundle, but it does not state when NOT to use this tool or mention alternatives. The CLI equivalence gives a hint, but there is no explicit context about choosing this over run_build or other sibling tools. It falls at 'implied usage' rather than providing clear guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each tool has a clearly distinct purpose: analyzing coverage, checking setup, providing guidance, generating a bundle, and building the site. There is no meaningful overlap between any of the five tools.
All tool names follow a consistent verb_noun pattern with clear verbs like analyze, check, get, and run. The naming is uniform and predictable.
Five tools is well-scoped for the phonebook preview workflow, covering setup, guidance, analysis, generation, and building without unnecessary extras or missing essentials.
The tools cover the full intended workflow: check environment, learn conventions, analyze coverage, generate previews, and build the gallery. There are no obvious dead ends or significant missing operations for this domain.
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