Component Library MCP

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool has a distinct purpose: checking duplicates, getting details, usage examples, import graph, listing, refreshing cache, and searching. No overlap, clear boundaries.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case (e.g., check_duplicate, list_components, refresh_cache), making them predictable and readable.
Tool Count5/5
Seven tools cover the essential operations for a component library: discovery, inspection, dependency analysis, and cache management. The scope is well-balanced, neither too sparse nor excessive.
Completeness3/5
The tool surface covers querying and analysis well (list, search, details, examples, dependencies) but lacks any creation or modification tools, which is a notable gap given the server's domain.
Average 3.3/5 across 7 of 7 tools scored. Lowest: 2.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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- No high-severity vulnerability alerts
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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?
No annotations are present, so the description must disclose behavioral traits. It mentions 'real usage examples' and 'snippets with context', but does not specify if it is read-only, performance implications, or what happens with limit. Minimal transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, concise and without redundancy. However, it is under-specified given the lack of parameter info, so it could be improved without sacrificing conciseness.
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?
With two parameters and no output schema, the description does not fully inform the agent. It omits return format, limit behavior, and edge cases, leaving the agent underinformed for a low-complexity tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, and the description does not explain the parameters at all. The agent has no guidance on what 'component_name' or 'limit' mean, despite 'limit' having an exclusiveMinimum constraint.
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 clearly states the tool retrieves real usage examples of a component from the codebase, specifying snippets with context. It is not explicitly differentiated from siblings, but the purpose is specific enough.
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 guidelines are provided about when to use this tool versus alternatives like search_components or get_component_details. The agent has no indication of when to choose this over sibling tools.
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?
No annotations exist, and the description does not disclose behavioral traits like whether the tool is read-only, any performance implications of depth, or required permissions. The agent has no information about side effects or limitations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences effectively front-load the action ('Get dependencies and dependents') and add context ('Shows composition patterns'). No wasted words.
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?
With three parameters, no output schema, and no annotations, the description is insufficient for complete understanding. It lacks details on parameter constraints, return format, and edge cases, making it hard for an agent to invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not explain the three parameters (component_name, direction, depth). The agent must infer what values to use, especially for direction and depth, without any guidance.
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 clearly indicates the tool retrieves dependencies and dependents for a component, distinguishing it from siblings like get_component_details (which gives broader details) and get_component_usage_example (which shows usage patterns). It mentions composition and page usage, adding specificity.
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 guidance on when to use this tool versus siblings. The description only states what it does, not context or alternatives. For example, it doesn't say when to prefer this over get_component_details or search_components.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It indicates a read operation (listing), which is appropriate. However, it lacks details about pagination, ordering, or whether the list is live or cached. For a simple list tool, this is adequate but not thorough.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise (two sentences), front-loading the core purpose and a usage hint. No unnecessary words, though it could be slightly more 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?
For a tool with four parameters and no output schema, the description is incomplete. It omits parameter explanations, output format, and any constraints. The agent would need additional information to use the tool effectively, especially given the parameter complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description does not explain any of the four parameters (directory, framework, min_usage, include_props). Since schema description coverage is 0%, the description fails to add meaning beyond the schema. The agent has no guidance on how to use these parameters, which is a critical gap.
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 clearly states it lists all components and specifies the summary info fields (name, path, prop count, usage count). The verb 'list' and resource 'components' are unambiguous, and the suggestion to call it first gives context, though it does not explicitly distinguish from sibling tools like search_components.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description advises to 'call this first to see what's available,' providing a general usage hint. However, it does not specify when not to use this tool (e.g., when filtering or searching is needed) or mention alternatives like search_components or get_component_details.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses that results are ranked, but lacks details on the ranking mechanism, potential side effects, authentication requirements, or result limits. The behavior is partially transparent but not comprehensive.
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 with two sentences. The first sentence front-loads the purpose and lists search criteria. No unnecessary words.
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?
For a tool with 5 parameters, no output schema, and no annotations, the description is insufficient. It lacks explanation of how multiple search criteria interact, result format, pagination, ranking details, and any constraints. More information is needed for an agent to use this tool reliably.
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 0%, so the description must add meaning beyond parameter names. It mentions the search modes (query, prop_name, prop_type, has_slot) which clarifies their role as search criteria. However, it does not explain parameter formats, combinations, or default behavior. This is adequate but not complete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches components by multiple criteria (natural-language query, prop name, prop type, slot name) and returns ranked results. This distinguishes it from sibling tools like list_components (lists all) and get_component_details (retrieves specific component info).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for searching components but does not explicitly state when to use this tool vs alternatives like list_components or get_component_details. No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. The description indicates a read operation (retrieving metadata) but does not explicitly state it is non-destructive or disclose potential errors, auth needs, or side effects. For a read-only tool, basic transparency is present but could be more explicit.
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?
Two sentences, no fluff. The main purpose is front-loaded, and every sentence adds value. Efficiently conveys key information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and no annotations, the description provides essential info but lacks details on return format, error handling, behavior when both parameters are used, and what happens if both are omitted. Adequate but not fully complete.
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 0%. The description adds meaning by stating that the tool accepts either 'name' or 'file_path' as identifiers, implying they are alternatives. However, it does not explain constraints, precedence if both are provided, or what happens if neither is given.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Get', the resource 'full metadata for a specific component', and lists specific metadata types (props, slots, events, description, story examples). It also mentions the two parameters, distinguishing it from sibling tools like 'list_components'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions the tool accepts component name or file_path but does not provide explicit guidance on when to use this tool versus siblings like 'list_components' or 'search_components'. No when-not-to-use or alternative suggestions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It states the tool is a 'check' and returns matches and a recommendation, implying read-only behavior. However, it does not explicitly confirm non-destructive nature or disclose any side effects, leaving some ambiguity.
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, consisting of two sentences that front-load the action and usage. Every word adds value, with no unnecessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of output schema, annotations, and parameter explanations, the description provides basic purpose and usage but omits details on what constitutes a duplicate or how the recommendation is structured. It is adequate but not fully complete for a tool of this complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not explain the parameters beyond their names (proposed_name, proposed_props, purpose). It fails to add meaning about formats, constraints, or usage rules for these parameters, which is insufficient for a tool with three parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Check if a proposed component would duplicate an existing one.' It also specifies when to call it ('BEFORE creating a new component') and what it returns ('similarity-ranked matches and a recommendation'), making it distinct from sibling tools like search_components.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: 'Call this BEFORE creating a new component.' This clearly indicates when to use the tool. However, it does not discuss when not to use it or suggest alternatives, but the context is sufficient for an AI agent to decide.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses incremental vs full scan behavior but omits details like side effects, performance impact, or safety of repeated calls.
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?
Two sentences with no wasted words, front-loaded purpose, and clear conditional guidance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one boolean param, the description covers the core behavior. Lacks context about what 'component files' are or any prerequisites, but still adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description explains the sole parameter (full_scan=true clears cache entirely), adding meaning beyond the schema's type-only definition.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's action ('force re-scan of all component files') and differentiates from siblings (check_duplicate, get_component_details, etc.) by its cache refresh function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains when to use the full_scan parameter (to clear cache entirely) but does not provide explicit guidance on when to use this tool vs alternatives, nor any prerequisites.
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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