What can storybook-mcp-server do?

component-metadata-extraction-and-indexing, story-argument-schema-generation, component-screenshot-capture-and-storage, mcp-tool-exposure-for-ai-assistants, story-variant-enumeration-and-filtering, component-dependency-graph-analysis, story-code-and-source-retrieval, multi-viewport-screenshot-generation, story-metadata-and-documentation-indexing, component-property-type-inference

storybook-mcp-server

MCP ServerFree

MCP server for Storybook - provides AI assistants access to components, stories, properties and screenshots

Open Source

/ 100

10 capabilities

Capabilities10 decomposed

component-metadata-extraction-and-indexing

Medium confidence

Extracts and indexes component metadata from Storybook's internal store, including component names, descriptions, properties, and story definitions. Works by connecting to a running Storybook instance via its API or reading the Storybook configuration and story files directly, then exposing this metadata through MCP tools that AI assistants can query to understand the component library structure and available properties.

Solves for

I want Claude to understand what components are available in my Storybook and their propertiesI need an AI assistant to know the exact prop types and descriptions for each componentI want to query my component library programmatically to find components by name or capability

Best for

design systems teams using Storybook as the single source of truth for components

AI-assisted code generation workflows that need accurate component APIs

teams building AI agents that generate component-based UIs

Requires

Storybook 6.0 or higher

Node.js 14+

Access to Storybook configuration files or running Storybook instance

Limitations

Requires Storybook instance to be running or pre-built static metadata available

Only extracts metadata that Storybook exposes — custom component properties not documented in stories won't be indexed

No real-time sync if component definitions change — requires server restart or manual refresh

What makes it unique

Bridges Storybook's internal component registry directly into MCP protocol, allowing AI assistants to query live component metadata without requiring separate documentation or API layers — integrates at the Storybook store level rather than parsing documentation

vs alternatives

More accurate than parsing README files or JSDoc comments because it reads Storybook's authoritative component definitions directly, and more maintainable than manual component registries because it auto-syncs with story definitions

story-argument-schema-generation

Medium confidence

Generates JSON Schema representations of Storybook story arguments (controls) by introspecting story definitions and their argTypes metadata. Uses Storybook's controls system to build machine-readable schemas that describe valid prop combinations, default values, and constraints for each story variant, enabling AI assistants to understand how to compose valid component instances.

Solves for

I want Claude to know what props each story accepts and their valid value rangesI need an AI to generate story argument objects that match the component's control definitionsI want to validate AI-generated prop combinations against the actual story schema

Best for

teams using Storybook controls for interactive component documentation

AI-assisted UI generation where prop validation is critical

design systems with complex component APIs that need precise prop documentation

Requires

Storybook 6.0+ with controls addon enabled

Stories written in CSF (Component Story Format) 3.0+

argTypes metadata defined in story files

Limitations

Only works with stories that have argTypes defined — stories without controls won't generate schemas

Custom control types may not map cleanly to JSON Schema — requires custom handlers for non-standard controls

Doesn't capture runtime validation logic or conditional prop dependencies

What makes it unique

Converts Storybook's argTypes control definitions into JSON Schema format, making story constraints machine-readable and queryable by AI models — treats Storybook controls as the source of truth for component prop contracts rather than requiring separate schema definitions

vs alternatives

More maintainable than TypeScript type extraction because it uses Storybook's already-documented controls as the single source of truth, and more flexible than static prop-types parsing because it captures runtime control configurations and constraints

component-screenshot-capture-and-storage

Medium confidence

Captures visual screenshots of Storybook stories using Puppeteer (headless browser automation) and stores them as indexed assets accessible via MCP. Renders each story in an isolated browser context, captures the rendered output at specified viewport sizes, and makes screenshots queryable by story name or component, enabling AI assistants to see what components actually look like visually.

Solves for

I want Claude to see what my components look like when rendered, not just their codeI need visual references for components so AI can make informed UI generation decisionsI want to generate screenshots of all story variants automatically for visual regression testing

Best for

design system teams that need AI-assisted visual component understanding

teams building AI agents that generate UI and need visual feedback loops

visual regression testing workflows integrated with AI code generation

Requires

Puppeteer 10.0+ or compatible headless browser

Storybook instance accessible at a URL (local or remote)

Disk space for screenshot storage (typically 50KB-500KB per screenshot)

Limitations

Requires headless browser environment — may not work in containerized/serverless contexts without additional configuration

Screenshot capture adds latency (typically 2-5 seconds per story) — not suitable for real-time interactions

Only captures static rendered output — doesn't capture interactive states or animations without custom viewport scripting

What makes it unique

Integrates Puppeteer-based screenshot automation directly into MCP protocol, allowing AI assistants to request and reference visual component representations on-demand — treats screenshots as first-class indexed assets in the component metadata store rather than separate artifacts

vs alternatives

More flexible than static screenshot galleries because screenshots are captured on-demand and can be regenerated, and more integrated than external visual testing tools because screenshots are indexed and queryable alongside component metadata

mcp-tool-exposure-for-ai-assistants

Medium confidence

Exposes Storybook component data through MCP (Model Context Protocol) tools that Claude and other AI assistants can call directly. Implements MCP resource and tool handlers that wrap component metadata, story arguments, and screenshot references into callable functions with defined input/output schemas, enabling seamless integration with Claude Desktop and other MCP-compatible AI platforms.

Solves for

I want Claude to be able to query my Storybook directly without me copying and pasting component infoI need Claude Desktop to have native access to my component library as MCP toolsI want to build AI agents that can autonomously explore and understand my component system

Best for

teams using Claude Desktop or other MCP-compatible AI assistants

developers building AI agents that need programmatic access to component libraries

organizations standardizing on MCP for AI tool integration

Requires

MCP server implementation (Node.js)

Claude Desktop 0.1.0+ or compatible MCP client

Network connectivity between MCP client and server

Limitations

MCP protocol overhead adds latency to each tool call (typically 50-200ms per request)

Tool schemas must be pre-defined — dynamic or user-defined tools require server-side schema registration

Limited to MCP-compatible AI platforms — won't work with non-MCP assistants like ChatGPT or Gemini without custom adapters

What makes it unique

Implements full MCP server specification for Storybook, exposing component operations as native MCP tools with proper schema validation and error handling — treats Storybook as an MCP resource provider rather than just a documentation source

vs alternatives

More native integration than REST API wrappers because it uses MCP's standardized tool protocol that Claude understands natively, and more maintainable than custom Claude plugins because it follows MCP conventions that work across multiple AI platforms

story-variant-enumeration-and-filtering

Medium confidence

Enumerates all story variants within Storybook and provides filtering/search capabilities to find specific stories by component name, story name, tags, or metadata. Builds an in-memory index of all stories from the Storybook configuration and exposes query tools that allow AI assistants to discover relevant stories without needing to know the exact story identifiers upfront.

Solves for

I want Claude to find all button variants without me listing them explicitlyI need to search for stories matching specific criteria (e.g., all 'disabled' states)I want an AI agent to discover what story variants exist for a given component

Best for

large design systems with hundreds of stories where manual discovery is impractical

AI-assisted workflows that need to explore component variants programmatically

teams building component search or discovery tools

Requires

Storybook 6.0+

Story files with consistent naming conventions for effective filtering

Sufficient memory for in-memory story index

Limitations

Index is built at server startup — new stories require server restart to be discoverable

Search is limited to metadata that Storybook exposes — custom story properties won't be searchable unless explicitly indexed

Performance degrades with very large story counts (1000+) — may need pagination or lazy loading

What makes it unique

Builds a queryable story index that supports multi-criteria filtering (component, variant, status, tags) rather than simple keyword search — enables AI assistants to discover stories programmatically without hardcoded story names

vs alternatives

More powerful than Storybook's built-in search UI because it exposes filtering as machine-readable queries that AI can compose dynamically, and more flexible than static story lists because it indexes all story metadata for multi-dimensional filtering

component-dependency-graph-analysis

Medium confidence

Analyzes component dependencies by parsing story files and component source code to build a dependency graph showing which components use other components. Exposes this graph through MCP tools, allowing AI assistants to understand component composition hierarchies and identify which components are safe to modify without breaking dependents.

Solves for

I want Claude to understand which components depend on a component I'm modifyingI need to know the full composition tree for a complex componentI want an AI to identify low-level components that are safe to refactor because they have few dependents

Best for

teams managing large component libraries where dependency tracking is critical

AI-assisted refactoring workflows that need impact analysis

design systems with complex component hierarchies

Requires

Access to component source files (JavaScript/TypeScript)

Consistent import/export patterns in component code

AST parser (built-in or via external library)

Limitations

Dependency analysis is static — doesn't capture runtime-only dependencies or dynamic imports

Requires component source files to be accessible — won't work with pre-built/compiled components

Accuracy depends on consistent import patterns — non-standard imports may be missed

What makes it unique

Builds a queryable component dependency graph from source code analysis rather than relying on manual documentation — enables AI to make informed decisions about component modification safety based on actual usage patterns

vs alternatives

More accurate than documentation-based dependency tracking because it analyzes actual imports, and more useful than generic code analysis tools because it's specifically optimized for component library structures

story-code-and-source-retrieval

Medium confidence

Retrieves and exposes the source code for stories and their underlying components through MCP tools. Allows AI assistants to read the actual implementation code for any story or component, including the story definition (CSF), component source, and any custom hooks or utilities used, enabling code-aware AI interactions.

Solves for

I want Claude to see the actual code for a story to understand how it worksI need an AI to read component source code to make informed refactoring suggestionsI want to ask Claude questions about specific component implementations

Best for

code-generation and refactoring workflows where AI needs to understand existing patterns

teams using AI for code review and analysis of component implementations

educational use cases where AI explains component code

Requires

Access to component source files (not compiled/minified)

File system access on the MCP server

Source maps or original source files for TypeScript components

Limitations

Requires source files to be accessible on the server — won't work with minified or compiled-only components

Large source files can exceed token limits in AI context windows — may need truncation or summarization

No syntax highlighting or formatting preservation — returns raw source code

What makes it unique

Exposes component and story source code as queryable MCP resources, allowing AI to read actual implementations rather than relying on documentation — treats source code as a first-class knowledge source alongside metadata

vs alternatives

More practical than asking developers to copy-paste code because AI can request it programmatically, and more accurate than documentation because it's the actual source of truth

multi-viewport-screenshot-generation

Medium confidence

Captures component screenshots across multiple viewport sizes (mobile, tablet, desktop) and device types, storing them indexed by viewport configuration. Uses Puppeteer to render stories at different screen dimensions and device emulations, enabling AI assistants to understand responsive behavior and make viewport-aware design decisions.

Solves for

I want Claude to see how my components look on mobile, tablet, and desktopI need visual references for responsive components at different breakpointsI want to generate responsive design documentation automatically with screenshots

Best for

teams building responsive design systems that need visual documentation

AI-assisted responsive UI generation workflows

design systems with mobile-first or responsive-first components

Requires

Puppeteer 10.0+ with device emulation support

Storybook instance accessible at a URL

Disk space for multiple screenshots per story (typically 150KB-1.5MB per story across viewports)

Limitations

Screenshot count multiplies with viewport variants — 10 stories × 3 viewports = 30 screenshots, increasing storage and capture time

Doesn't capture touch interactions or hover states specific to mobile — only visual layout

Device emulation may not perfectly match real device rendering — CSS media queries are tested but not actual device hardware

What makes it unique

Captures and indexes screenshots across multiple viewports as a first-class feature, allowing AI to reason about responsive behavior — treats viewport variants as important as story variants rather than as an afterthought

vs alternatives

More comprehensive than single-viewport screenshots because it captures responsive behavior, and more automated than manual responsive testing because it generates all viewport variants in one batch

story-metadata-and-documentation-indexing

Medium confidence

Indexes story-level metadata including titles, descriptions, tags, and custom documentation fields defined in story files. Builds a searchable metadata index that allows AI assistants to find stories by description, purpose, or custom tags, and to understand the intended use case for each story variant.

Solves for

I want Claude to find stories by their purpose or use case, not just by nameI need to tag stories with custom metadata that AI can use for discoveryI want story descriptions to be searchable and queryable by AI

Best for

design systems with well-documented stories and consistent metadata practices

teams using story tags for organization and discovery

AI-assisted workflows that need semantic understanding of story purpose

Requires

Storybook 6.0+ with story metadata support

Consistent story documentation practices

Story files with title, description, and tags defined

Limitations

Requires consistent metadata practices across stories — inconsistent tagging reduces search effectiveness

Custom metadata fields must be explicitly defined in story files — no automatic extraction from comments

Search is literal/keyword-based — doesn't understand semantic meaning of descriptions

What makes it unique

Indexes story-level metadata (descriptions, tags, documentation) as queryable knowledge, allowing AI to discover stories by purpose rather than just by name — treats story documentation as machine-readable metadata rather than human-only text

vs alternatives

More discoverable than stories without metadata because AI can search by purpose, and more maintainable than hardcoded story lists because metadata lives in story files and stays in sync

component-property-type-inference

Medium confidence

Infers and exposes component property types by analyzing TypeScript/JSDoc definitions, Storybook argTypes, and story usage patterns. Builds a type system representation that AI assistants can query to understand valid prop types, optional vs required props, and prop constraints without needing to parse TypeScript directly.

Solves for

I want Claude to know the exact types for all component propsI need an AI to understand which props are required vs optionalI want to validate AI-generated prop combinations against the component's type system

Best for

TypeScript-based design systems where type safety is critical

AI-assisted code generation that needs to respect component type contracts

teams building type-aware component composition tools

Requires

TypeScript source files or JSDoc annotations in component code

TypeScript compiler or type parser (built-in or via external library)

Storybook argTypes for prop documentation

Limitations

Type inference is best-effort — complex types (unions, generics, conditional types) may not be fully captured

Requires TypeScript source files or JSDoc annotations — JavaScript-only components won't have type information

Doesn't capture runtime validation or custom prop validators — only static type information

What makes it unique

Extracts and exposes component type information as queryable metadata rather than requiring AI to parse TypeScript — makes type contracts machine-readable and accessible through MCP tools

vs alternatives

More accurate than JSDoc parsing alone because it combines TypeScript type information with Storybook argTypes, and more useful than raw type definitions because it's normalized into a queryable format

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

Artifacts that share capabilities with storybook-mcp-server, ranked by overlap. Discovered automatically through the match graph.

Platform40

Chromatic

Visual testing and review platform built on Storybook.

component metadata and api documentation extractionsnapshot configuration and capture customization via story parameters

2 shared capabilities

MCP Server39

shadcn-ui-mcp-server

A mcp server to allow LLMS gain context about shadcn ui component structure,usage and installation,compaitable with react,svelte 5,vue & React Native

component source code parsing and schema validationcomponent metadata and documentation indexing

2 shared capabilities

Product38

Builder.io

AI visual development with design-to-code and CMS.

storybook integration and story generationcomponent library publishing and distribution

2 shared capabilities

Repository52

openui

OpenUI let's you describe UI using your imagination, then see it rendered live.

image-reference-guided-component-generationcomponent-sharing-via-s3-storage

2 shared capabilities

MCP Server24

Storyblok

** - Storyblok MCP server enables your AI assistants to directly access and manage your Storyblok spaces, stories, components, assets, workflows, and more.

component schema introspection and validation

1 shared capability

MCP Server28

@coinbase/cds-mcp-server

Coinbase Design System - MCP Server

component-metadata-extraction-and-schema-generation

1 shared capability

Best For

✓design systems teams using Storybook as the single source of truth for components
✓AI-assisted code generation workflows that need accurate component APIs
✓teams building AI agents that generate component-based UIs
✓teams using Storybook controls for interactive component documentation
✓AI-assisted UI generation where prop validation is critical
✓design systems with complex component APIs that need precise prop documentation
✓design system teams that need AI-assisted visual component understanding
✓teams building AI agents that generate UI and need visual feedback loops

Known Limitations

⚠Requires Storybook instance to be running or pre-built static metadata available
⚠Only extracts metadata that Storybook exposes — custom component properties not documented in stories won't be indexed
⚠No real-time sync if component definitions change — requires server restart or manual refresh
⚠Only works with stories that have argTypes defined — stories without controls won't generate schemas
⚠Custom control types may not map cleanly to JSON Schema — requires custom handlers for non-standard controls
⚠Doesn't capture runtime validation logic or conditional prop dependencies

Requirements

Storybook 6.0 or higherNode.js 14+Access to Storybook configuration files or running Storybook instanceStorybook 6.0+ with controls addon enabledStories written in CSF (Component Story Format) 3.0+argTypes metadata defined in story filesPuppeteer 10.0+ or compatible headless browserStorybook instance accessible at a URL (local or remote)

Input / Output

Accepts: Storybook configuration (JSON/TypeScript), Story files (CSF format), Component source files, Storybook story definitions (CSF), argTypes metadata objects, Storybook story URLs, viewport dimensions (width, height), story identifiers, MCP tool call requests with JSON arguments, component names or identifiers, query filters, search query strings, filter criteria (component name, tags, status), pagination parameters, component names or file paths, dependency direction (dependents or dependencies), viewport configurations (width, height, device type), device emulation profiles, story metadata queries, tag filters, description search terms, component names, prop names

Produces: structured JSON metadata, component property schemas, story definitions with arguments, JSON Schema documents, prop validation schemas, argument type definitions, PNG/JPEG image files, image metadata (dimensions, capture timestamp), screenshot index/manifest, JSON-formatted tool responses, component metadata objects, screenshot URIs or embedded image data, story metadata arrays, filtered story lists, story identifiers and URLs, dependency graph JSON, component dependency lists, impact analysis reports, source code strings, code with line numbers, syntax-highlighted code blocks, screenshot files indexed by viewport, responsive design metadata, viewport-indexed image manifests, story metadata objects, tagged story lists, metadata search results, type definitions (JSON representation), prop type schemas, required/optional prop lists

UnfragileRank

Adoption15%(30% weight)

Quality28%(25% weight)

Ecosystem50%(25% weight)

Match Graph10%(15% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: MCP Server

10 capabilities

Visit storybook-mcp-server→

Package Details

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0.1.3

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MCP server for Storybook - provides AI assistants access to components, stories, properties and screenshots

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Capabilities10 decomposed

component-metadata-extraction-and-indexing

Medium confidence

Solves for

Best for

design systems teams using Storybook as the single source of truth for components

AI-assisted code generation workflows that need accurate component APIs

teams building AI agents that generate component-based UIs

Requires

Storybook 6.0 or higher

Node.js 14+

Access to Storybook configuration files or running Storybook instance

Limitations

Requires Storybook instance to be running or pre-built static metadata available

Only extracts metadata that Storybook exposes — custom component properties not documented in stories won't be indexed

No real-time sync if component definitions change — requires server restart or manual refresh

What makes it unique

vs alternatives

story-argument-schema-generation

Medium confidence

Solves for

Best for

teams using Storybook controls for interactive component documentation

AI-assisted UI generation where prop validation is critical

design systems with complex component APIs that need precise prop documentation

Requires

Storybook 6.0+ with controls addon enabled

Stories written in CSF (Component Story Format) 3.0+

argTypes metadata defined in story files

Limitations

Only works with stories that have argTypes defined — stories without controls won't generate schemas

Custom control types may not map cleanly to JSON Schema — requires custom handlers for non-standard controls

Doesn't capture runtime validation logic or conditional prop dependencies

What makes it unique

vs alternatives

component-screenshot-capture-and-storage

Medium confidence

Solves for

Best for

design system teams that need AI-assisted visual component understanding

teams building AI agents that generate UI and need visual feedback loops

visual regression testing workflows integrated with AI code generation

Requires

Puppeteer 10.0+ or compatible headless browser

Storybook instance accessible at a URL (local or remote)

Disk space for screenshot storage (typically 50KB-500KB per screenshot)

Limitations

Requires headless browser environment — may not work in containerized/serverless contexts without additional configuration

Screenshot capture adds latency (typically 2-5 seconds per story) — not suitable for real-time interactions

Only captures static rendered output — doesn't capture interactive states or animations without custom viewport scripting

What makes it unique

vs alternatives

mcp-tool-exposure-for-ai-assistants

Medium confidence

Solves for

Best for

teams using Claude Desktop or other MCP-compatible AI assistants

developers building AI agents that need programmatic access to component libraries

organizations standardizing on MCP for AI tool integration

Requires

MCP server implementation (Node.js)

Claude Desktop 0.1.0+ or compatible MCP client

Network connectivity between MCP client and server

Limitations

MCP protocol overhead adds latency to each tool call (typically 50-200ms per request)

Tool schemas must be pre-defined — dynamic or user-defined tools require server-side schema registration

Limited to MCP-compatible AI platforms — won't work with non-MCP assistants like ChatGPT or Gemini without custom adapters

What makes it unique

vs alternatives

story-variant-enumeration-and-filtering

Medium confidence

Solves for

Best for

large design systems with hundreds of stories where manual discovery is impractical

AI-assisted workflows that need to explore component variants programmatically

teams building component search or discovery tools

Requires

Storybook 6.0+

Story files with consistent naming conventions for effective filtering

Sufficient memory for in-memory story index

Limitations

Index is built at server startup — new stories require server restart to be discoverable

Search is limited to metadata that Storybook exposes — custom story properties won't be searchable unless explicitly indexed

Performance degrades with very large story counts (1000+) — may need pagination or lazy loading

What makes it unique

vs alternatives

component-dependency-graph-analysis

Medium confidence

Solves for

Best for

teams managing large component libraries where dependency tracking is critical

AI-assisted refactoring workflows that need impact analysis

design systems with complex component hierarchies

Requires

Access to component source files (JavaScript/TypeScript)

Consistent import/export patterns in component code

AST parser (built-in or via external library)

Limitations

Dependency analysis is static — doesn't capture runtime-only dependencies or dynamic imports

Requires component source files to be accessible — won't work with pre-built/compiled components

Accuracy depends on consistent import patterns — non-standard imports may be missed

What makes it unique

vs alternatives

story-code-and-source-retrieval

Medium confidence

Solves for

Best for

code-generation and refactoring workflows where AI needs to understand existing patterns

teams using AI for code review and analysis of component implementations

educational use cases where AI explains component code

Requires

Access to component source files (not compiled/minified)

File system access on the MCP server

Source maps or original source files for TypeScript components

Limitations

Requires source files to be accessible on the server — won't work with minified or compiled-only components

Large source files can exceed token limits in AI context windows — may need truncation or summarization

No syntax highlighting or formatting preservation — returns raw source code

What makes it unique

vs alternatives

More practical than asking developers to copy-paste code because AI can request it programmatically, and more accurate than documentation because it's the actual source of truth

multi-viewport-screenshot-generation

Medium confidence

Solves for

Best for

teams building responsive design systems that need visual documentation

AI-assisted responsive UI generation workflows

design systems with mobile-first or responsive-first components

Requires

Puppeteer 10.0+ with device emulation support

Storybook instance accessible at a URL

Disk space for multiple screenshots per story (typically 150KB-1.5MB per story across viewports)

Limitations

Screenshot count multiplies with viewport variants — 10 stories × 3 viewports = 30 screenshots, increasing storage and capture time

Doesn't capture touch interactions or hover states specific to mobile — only visual layout

Device emulation may not perfectly match real device rendering — CSS media queries are tested but not actual device hardware

What makes it unique

vs alternatives

More comprehensive than single-viewport screenshots because it captures responsive behavior, and more automated than manual responsive testing because it generates all viewport variants in one batch

story-metadata-and-documentation-indexing

Medium confidence

Solves for

Best for

design systems with well-documented stories and consistent metadata practices

teams using story tags for organization and discovery

AI-assisted workflows that need semantic understanding of story purpose

Requires

Storybook 6.0+ with story metadata support

Consistent story documentation practices

Story files with title, description, and tags defined

Limitations

Requires consistent metadata practices across stories — inconsistent tagging reduces search effectiveness

Custom metadata fields must be explicitly defined in story files — no automatic extraction from comments

Search is literal/keyword-based — doesn't understand semantic meaning of descriptions

What makes it unique

vs alternatives

More discoverable than stories without metadata because AI can search by purpose, and more maintainable than hardcoded story lists because metadata lives in story files and stays in sync

component-property-type-inference

Medium confidence

Solves for

Best for

TypeScript-based design systems where type safety is critical

AI-assisted code generation that needs to respect component type contracts

teams building type-aware component composition tools

Requires

TypeScript source files or JSDoc annotations in component code

TypeScript compiler or type parser (built-in or via external library)

Storybook argTypes for prop documentation

Limitations

Type inference is best-effort — complex types (unions, generics, conditional types) may not be fully captured

Requires TypeScript source files or JSDoc annotations — JavaScript-only components won't have type information

Doesn't capture runtime validation or custom prop validators — only static type information

What makes it unique

Extracts and exposes component type information as queryable metadata rather than requiring AI to parse TypeScript — makes type contracts machine-readable and accessible through MCP tools

vs alternatives

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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storybook-mcp-server

Capabilities10 decomposed

component-metadata-extraction-and-indexing

story-argument-schema-generation

component-screenshot-capture-and-storage

mcp-tool-exposure-for-ai-assistants

story-variant-enumeration-and-filtering

component-dependency-graph-analysis

story-code-and-source-retrieval

multi-viewport-screenshot-generation

story-metadata-and-documentation-indexing

component-property-type-inference

Related Artifactssharing capabilities

Chromatic

shadcn-ui-mcp-server

Builder.io

openui

Storyblok

@coinbase/cds-mcp-server

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Package Details

About

Categories

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Are you the builder of storybook-mcp-server?

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storybook-mcp-server

Capabilities10 decomposed

component-metadata-extraction-and-indexing

story-argument-schema-generation

component-screenshot-capture-and-storage

mcp-tool-exposure-for-ai-assistants

story-variant-enumeration-and-filtering

component-dependency-graph-analysis

story-code-and-source-retrieval

multi-viewport-screenshot-generation

story-metadata-and-documentation-indexing

component-property-type-inference

Related Artifactssharing capabilities

Chromatic

shadcn-ui-mcp-server

Builder.io

openui

Storyblok

@coinbase/cds-mcp-server

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Package Details

About

Categories

Alternatives to storybook-mcp-server

Are you the builder of storybook-mcp-server?

Get the weekly brief

Data Sources