Storybook MCP Server
The Storybook MCP Server facilitates integration between Storybook components and external tools (like AI) to enhance UI development workflows. It exposes component data via MCP, allowing you to:
List all components: Retrieve all available components from Storybook using the
list-componentstoolSearch components by name: Find components by name or keyword using the
find-components-by-nametool, supporting partial matchesFacilitate UI component discovery based on design contexts from Figma
Recommended as a runtime environment for installing dependencies and building the MCP server.
Supported as an alternative package manager for installing dependencies and building the MCP server.
Provides an MCP server that integrates with Storybook, allowing AI tools to query available UI components, retrieve component information and usage examples to help implement UI from design specifications.
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., "@Storybook MCP Serverfind components for a login form"
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.
Storybook MCP Server
⚠️ This tool was created for testing purposes and is not recommended for use in production environments.
Getting Started
Before you begin, you need to prepare your Storybook static files. https://storybook.js.org/docs/sharing/publish-storybook
1. Clone the repository
git clone https://github.com/m-yoshiro/storybook-mcp.git
cd storybook-mcp2. Install dependencies
We recommend using Bun
bun install
# or
npm install3. Build
bun run build
# or
npm run build4. Set up
{
"mcpServers": {
"storybook-mcp": {
"command": "node",
"args": [
"/< your path>/index.js",
// Optional: path to your Storybook static json file
"/< your path>/index.json"
]
}
}
}The server will load your Storybook data and expose MCP tools to external agents.
Related MCP server: MCP Boilerplate
🔧 Available Tools
Tool Name | Description | Parameters |
| Lists all available components from Storybook |
|
| Finds components based on a keyword (partial match supported) |
|
Available Tools
2 toolsfind-components-by-nameC
Search components by name/keyword
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Component name or keyword to search for | |
| path | No | Path to the index.json or stories.json file (optional if default path is provided) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only states the search action without disclosing behavioral traits such as search scope (e.g., partial/full matches), result format, pagination, or error handling. It adds little beyond the basic operation.
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, efficient sentence with zero waste, clearly front-loading the tool's purpose. It is appropriately sized for a simple search tool, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete for a search tool. It lacks details on behavioral aspects like result format, search behavior, or error cases, leaving gaps in understanding how to effectively use the tool.
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 schema already documents both parameters ('name' and 'path'). The description adds no additional meaning beyond implying 'name' is used for searching, which aligns with the schema. Baseline 3 is appropriate as the schema handles parameter documentation.
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 'Search components by name/keyword' clearly states the action (search) and target resource (components) with a specific criterion (name/keyword). It distinguishes from the sibling 'list-components' by implying filtering rather than enumeration, though not explicitly contrasting 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?
No explicit guidance on when to use this tool versus alternatives like 'list-components' is provided. The description implies usage for searching by name, but lacks context on prerequisites, exclusions, or comparative scenarios, offering minimal direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list-componentsC
Returns all available components
| Name | Required | Description | Default |
|---|---|---|---|
| path | No | Path to the index.json or stories.json file (optional if default path is provided) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. 'Returns all available components' implies a read-only operation but doesn't specify whether this is a safe operation, what format the return takes, whether there are rate limits, or any error conditions. For a tool with zero annotation coverage, this leaves significant behavioral 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 extremely concise at just four words. It's front-loaded with the core purpose and contains no wasted words. While it may be under-specified, it's not verbose or poorly structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no annotations, no output schema, and a sibling tool exists, the description is incomplete. It doesn't explain what 'components' are, what 'available' means, how results are returned, or when to use this versus the sibling tool. For a tool that presumably returns data, the lack of output information is a significant gap.
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 description coverage is 100%, so the schema already documents the single optional 'path' parameter. The description adds no parameter information beyond what the schema provides. According to the scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description.
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 the basic purpose ('Returns all available components') which is clear but vague. It specifies the verb 'Returns' and resource 'components' but doesn't distinguish from the sibling tool 'find-components-by-name' or provide any scope details about what 'available' means. This is adequate but has clear gaps in differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus the sibling 'find-components-by-name' tool. There's no mention of alternatives, prerequisites, or context for usage. The agent would have to infer usage patterns from the tool names alone, which is insufficient guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
The two tools have clearly distinct purposes: 'find-components-by-name' searches for specific components based on a name or keyword, while 'list-components' returns all available components without filtering. There is no overlap or ambiguity between these operations.
Both tools follow a consistent verb_noun pattern with hyphen-separated words: 'find-components-by-name' and 'list-components'. The naming is predictable and readable throughout the set.
With only 2 tools, the server feels thin for a Storybook domain, which typically involves browsing, testing, and managing UI components. This limited set may not cover common workflows like viewing component details, running tests, or interacting with stories.
The tool surface is severely incomplete for a Storybook server. It lacks essential operations such as getting component details, listing or running stories, viewing documentation, or managing component states. Agents will face dead ends when trying to perform basic Storybook tasks.
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