Basecoat UI MCP Server

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Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Every tool has a clearly distinct purpose with no ambiguity. For example, get_component retrieves HTML code, get_usage provides documentation, and list_components organizes components by category. The descriptions make it easy to differentiate between tools like get_category (components in a category) and search_components (search by name/category).
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern with snake_case, such as get_category, list_components, and search_components. The verbs (get, list, search) are used appropriately and predictably across the set, making the naming scheme highly readable and uniform.
Tool Count5/5
With 7 tools, the count is well-scoped for a UI component library server, covering core operations like listing, searching, retrieving code, and documentation. Each tool earns its place without feeling excessive or insufficient, providing a balanced surface for interacting with Basecoat components.
Completeness4/5
The tool set offers strong coverage for retrieving and exploring Basecoat components, including setup, themes, and usage. A minor gap exists in lacking update or customization tools (e.g., modify components), but agents can work around this as the server focuses on read-only access to a static library.
Average 3/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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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 provided, so the description carries the full burden of behavioral disclosure. It mentions retrieving documentation but does not specify format (e.g., markdown, HTML), permissions required, rate limits, or error handling. For a read operation with zero annotation coverage, this lacks essential context about how the tool behaves.
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: 'Get usage documentation for a component.' It is front-loaded with the core action and resource, with zero wasted words. This is appropriately sized for a simple tool with one parameter.
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's simplicity (one parameter, no output schema, no annotations), the description is incomplete. It does not explain what 'usage documentation' includes, how results are returned, or differentiate from siblings. For a tool in a set with multiple similar options, more context is needed to guide effective use.
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 input schema has 100% description coverage, with the 'component' parameter documented as 'Component name (e.g., "button", "card", "input").' The description adds no additional meaning beyond this, such as valid component types or examples. Baseline 3 is appropriate since the schema does the heavy lifting.
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 tool's purpose as 'Get usage documentation for a component,' which is clear but vague. It specifies the verb 'Get' and resource 'usage documentation,' but lacks detail on what 'usage documentation' entails or how it differs from similar tools like 'get_component' or 'get_category.' This provides basic understanding but no sibling 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 offers no guidance on when to use this tool versus alternatives. With sibling tools like 'get_component' and 'get_category' available, it fails to specify contexts, prerequisites, or exclusions. This leaves the agent without direction on selecting the appropriate tool for documentation needs.
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 for behavioral disclosure. It states a read operation ('Get'), implying it's likely non-destructive, but doesn't address permissions, rate limits, pagination, error handling, or what 'all components' entails (e.g., format, completeness). This leaves significant gaps for a tool with potential behavioral complexity.
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, clear sentence with zero wasted words. It's front-loaded with the core purpose and efficiently conveys the essential information without unnecessary elaboration.
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 tool that retrieves data. It doesn't explain what 'components' are, the return format, or any behavioral traits like pagination or errors. For a read operation with potential complexity, this leaves the agent under-informed.
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%, with the single parameter 'category' well-documented in the schema (including allowed values). The description adds no additional parameter semantics beyond implying filtering by category, so it meets the baseline for high schema coverage without compensating value.
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 action ('Get all components') and target resource ('in a specific category'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'list_components' or 'search_components' that might also retrieve components, which prevents a perfect score.
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 alternatives like 'list_components' or 'search_components'. It doesn't mention prerequisites, exclusions, or specific contexts for usage, leaving the agent to infer based on tool names alone.
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 are provided, so the description carries the full burden of behavioral disclosure. It states the tool searches by name or category, but doesn't describe key behaviors such as whether it returns partial matches, supports pagination, has rate limits, requires authentication, or what the output format looks like (e.g., list of objects). For a search tool with zero annotation coverage, this is a significant gap in transparency.
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 and front-loaded with a single, clear sentence: 'Search for components by name or category.' There is no wasted text, repetition, or unnecessary elaboration, making it efficient and easy to parse for an AI agent.
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's complexity (a search function with one parameter), lack of annotations, and no output schema, the description is incomplete. It doesn't explain the return values, behavioral traits like search specificity or result limits, or how it differs from sibling tools. For a tool that likely returns multiple results, more context is needed to guide effective usage.
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 description adds minimal value beyond the input schema, which has 100% coverage for the single parameter 'query'. It mentions searching 'by name or category,' which loosely relates to the query parameter but doesn't provide additional syntax, examples beyond the schema's description, or constraints. With high schema coverage, the baseline is 3, as the schema already documents the parameter adequately.
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's purpose as 'Search for components by name or category,' which includes a specific verb ('search') and resource ('components'). It distinguishes the search functionality from siblings like 'list_components' (which likely lists without filtering) and 'get_component' (which retrieves a single component). However, it doesn't explicitly differentiate from 'get_category' or other siblings, keeping it from a perfect score.
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 alternatives. It doesn't mention when to choose 'search_components' over 'list_components' (e.g., for filtered results vs. full listing) or 'get_component' (e.g., for specific IDs vs. queries). There's no context on prerequisites, exclusions, or typical use cases, leaving the agent to infer usage from the name alone.
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 are provided, so the description carries the full burden of behavioral disclosure. It states the tool retrieves HTML code, implying a read-only operation, but doesn't disclose other traits like authentication needs, rate limits, error handling, or what happens if the component doesn't exist. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.
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 that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action ('Get HTML code'), making it easy to understand at a glance. Every part of the sentence earns its place.
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 tool's low complexity (1 parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on usage guidelines, behavioral traits, and output format. Without annotations or an output schema, the description should ideally explain more about what the HTML code includes or how it's returned, but it meets the minimum for a simple retrieval 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?
The input schema has 100% description coverage, with the 'name' parameter clearly documented. The description doesn't add any semantic details beyond what the schema provides (e.g., it doesn't explain the format of component names or provide examples). With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but doesn't need to.
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's purpose with a specific verb ('Get') and resource ('HTML code for a specific Basecoat component'). It distinguishes from siblings like 'list_components' (which likely lists components) and 'search_components' (which likely searches), but doesn't explicitly differentiate from 'get_category' or 'get_usage', which might also retrieve component-related data. The purpose is clear but sibling differentiation is partial.
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 alternatives. It doesn't mention when to use 'get_component' over 'list_components' or 'search_components', nor does it specify prerequisites or exclusions. Usage is implied by the name and purpose, but explicit guidelines are missing.
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 are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'gets' code, implying a read-only operation, but doesn't specify any behavioral traits like whether it requires authentication, has rate limits, returns static or dynamic content, or if it's idempotent. For a tool with zero annotation coverage, this is a significant gap in transparency.
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: 'Get Basecoat CSS setup code with CDN links.' It's front-loaded with the core purpose, has zero wasted words, and is appropriately sized for a simple tool. Every part of the sentence contributes directly to understanding the tool's function.
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 tool's simplicity (0 parameters, no annotations, no output schema), the description is minimally adequate. It states what the tool does, but lacks context on usage, behavioral details, or output format. For a tool that likely returns code snippets, the absence of an output schema means the description should ideally hint at the return type, but it doesn't. This makes it complete enough for basic understanding but with clear gaps.
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?
The tool has 0 parameters, and the schema description coverage is 100% (since there are no parameters to describe). In such cases, the baseline score is 4, as there's no need for the description to compensate for parameter documentation. The description doesn't add parameter details, but that's appropriate given the lack of parameters.
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's purpose: 'Get Basecoat CSS setup code with CDN links.' It specifies the action ('Get'), the resource ('Basecoat CSS setup code'), and the format ('with CDN links'). However, it doesn't explicitly differentiate this from sibling tools like 'get_theme_script' or 'get_usage', which might also retrieve code-related resources, so it doesn't reach a perfect score.
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 alternatives. It doesn't mention any context, prerequisites, or exclusions, such as whether this is for initial setup only or if it's needed for specific environments. With sibling tools like 'get_theme_script' and 'get_usage', there's no indication of how this tool differs in usage scenarios.
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 the full burden of behavioral disclosure. It states the tool retrieves a script but doesn't clarify if it's a read-only operation, what format the script is in (e.g., JavaScript, CSS), or any side effects like caching. This leaves significant gaps for a tool with zero annotation coverage.
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 no wasted words, clearly front-loading the core purpose. It's appropriately sized for a simple tool with no parameters.
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 tool's low complexity (0 parameters, no output schema, no annotations), the description is minimally adequate but incomplete. It lacks details on the script's format, usage context, or return behavior, which are needed for full understanding despite the simple schema.
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?
The tool has 0 parameters with 100% schema description coverage, so the schema fully documents the absence of inputs. The description adds no parameter information, which is appropriate here, and the baseline for 0 parameters is 4, as no compensation is needed.
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's purpose with a specific verb ('Get') and resource ('theme switcher script'), specifying it's for 'dark/light mode'. It distinguishes from siblings like 'get_category' or 'get_component' by focusing on theme functionality, though it doesn't explicitly contrast 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 guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, context, or exclusions, such as whether it's for initial setup or runtime switching, leaving usage entirely implied.
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 are provided, so the description carries full burden. It states the tool lists components 'organized by category', hinting at structured output, but doesn't disclose behavioral traits like pagination, rate limits, authentication needs, or whether it's a read-only operation. The description is minimal, leaving gaps in understanding how the tool behaves.
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 that front-loads the purpose ('List all available Basecoat components') and adds useful detail ('organized by category') without waste. Every word earns its place, making it highly concise and well-structured.
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 tool has 0 parameters, no annotations, and no output schema, the description is adequate for a simple list operation but lacks completeness. It doesn't explain return values (e.g., format of listed components) or address potential complexities like large result sets, leaving room for improvement in contextual detail.
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?
The tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description doesn't add param info, which is appropriate, but it does imply the output includes categorization, adding slight semantic value beyond the empty schema.
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 action ('List') and resource ('Basecoat components'), specifying they are 'organized by category'. It distinguishes from siblings like 'get_component' (single component) and 'search_components' (filtered search), but doesn't explicitly contrast with 'get_category' (which might retrieve category details).
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 'search_components' for filtered results or 'get_category' for category-specific data. The description implies it's for retrieving all components broadly, but lacks context on prerequisites or exclusions.
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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