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m-yoshiro

Storybook MCP Server

by m-yoshiro
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Hybrid

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    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.

    Naming Consistency5/5

    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.

    Tool Count2/5

    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.

    Completeness2/5

    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.

  • Average 2.8/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior2/5

    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.

    Conciseness5/5

    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.

    Completeness2/5

    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.

    Parameters3/5

    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.

    Purpose3/5

    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.

    Usage Guidelines2/5

    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.

  • Behavior2/5

    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.

    Conciseness5/5

    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.

    Completeness2/5

    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.

    Parameters3/5

    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.

    Purpose4/5

    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.

    Usage Guidelines2/5

    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.

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Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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