tailwind-context-resolver-mcp

by vola-trebla

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Latest release: v0.2.0
Disambiguation5/5
Each tool targets a distinct aspect of Tailwind CSS context: config overview, theme variable extraction, dead-token auditing, class compilation simulation, theme token querying, class string validation, and conflict detection. There is no meaningful overlap; even the two token-related tools are clearly differentiated by their source (CSS @theme vs resolved theme).
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern using lowercase with underscores (get_, resolve_, audit_, simulate_, validate_, detect_). Though the verbs differ, they accurately reflect the unique action each tool performs, and the pattern is uniform throughout.
Tool Count5/5
Seven tools is well within the ideal range and each tool directly serves the server's purpose of resolving and validating Tailwind CSS context. No redundant tools and no missing essential operations for a read-only context resolver.
Completeness5/5
The toolset covers the full lifecycle of working with Tailwind design tokens and classes: config summary, theme token extraction and querying, dead-token auditing, class validation, conflict detection, and JIT simulation. There are no obvious gaps; it handles both Tailwind v3/v4 scenarios and provides actionable checks for developers.
Average 4.1/5 across 7 of 7 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 is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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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, the description carries the full burden of explaining side effects, safety, and error behavior. It only states what is returned and provides no information about permissions, failure modes, or whether the operation is read-only.
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: the first delivers the core purpose with specific output contents, the second gives a usage recommendation. Every word earns its place.
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?
The tool is simple with one well-documented param and no output schema. The description covers the purpose and usage position well. It could mention error handling or behavior when config_path is invalid, but for a summary tool the description is adequate.
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 fully describes the single parameter (config_path) with an absolute path requirement. The description adds no additional parameter meaning, but schema coverage is 100%, so the baseline of 3 is appropriate.
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 returns a compact overview of the Tailwind configuration, listing specific elements (version, prefix, theme sections, plugins). This distinguishes it from sibling tools that focus on resolving variables, auditing usage, or detecting conflicts.
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 guidance to use this tool first to understand the design system before querying specific tokens. It doesn't explicitly name alternatives or exclusions, but the 'use first' instruction gives clear contextual guidance.
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 must carry the full burden. It discloses that compilation occurs and that generated CSS rules are returned, but it does not address failure modes, side effects, file existence requirements, or whether the operation is non-mutating. The 'Validates' wording hints at a safe read-only behavior, but this is not confirmed.
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 three sentences, front-loaded with the core action, followed by supported features and return value. No unnecessary words or repetition. It is concise and well-structured.
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?
The description covers purpose, supported cases, and return value. Since there is no output schema, the explicit mention of generated CSS rules is helpful. It does not address edge cases or error handling, but for a 3-parameter tool with fully documented schema, it is sufficiently 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 coverage is 100% with descriptions for all three parameters. The description adds context about supported features (arbitrary values, JIT modifiers, custom plugin classes) but does not add per-parameter semantics beyond what the schema already provides. Therefore, baseline 3 is appropriate.
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 uses a specific verb 'Validates' and 'compiling' with a clear resource ('Tailwind utility classes against the JIT engine'). It distinguishes from sibling validate_class_string by emphasizing programmatic compilation and the generation of CSS rules, making its unique role clear.
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 validating and compiling Tailwind classes but does not explicitly state when to prefer it over alternatives like validate_class_string or when not to use it. No exclusions or alternate tool references are provided, so guidance is only implicit.
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 of disclosure. It explains the scanning and reference-checking behavior, which implies a non-mutating audit. However, it does not state what the tool returns, whether any files are modified, or if there are performance implications.
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 concise sentences that front-load the core purpose and then provide methodological detail. No wasted words.
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?
The description covers the tool's purpose and method adequately, but without an output schema or annotations, it omits what the user receives (list? count?). This leaves the description slightly incomplete for an agent to fully anticipate the tool's behavior.
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 already provides 100% descriptive coverage for all three parameters. The description adds context about '@theme blocks' and 'source code' but does not clarify scan_dirs behavior or expected output, so it adds little beyond the baseline.
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 identifies dead design tokens, with a specific verb and resource. It explains the method (scanning CSS custom properties in @theme blocks) and distinguishes itself from sibling tools like resolve_theme_tokens and detect_css_conflicts.
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 clearly implies when to use the tool: when checking for unused design tokens. It doesn't explicitly name alternatives or when-not-to-use cases, but the context is strong enough that an agent would know when to select it over related theme tools.
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 provided, the description carries the full burden of behavioral disclosure. It explains the core detection action and provides examples, but it does not disclose details such as reliance on the Tailwind config file, return format, or error behavior. This is a moderate disclosure gap for a tool without annotations.
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 two sentences, front-loaded with the primary action, and includes an example plus a clear use case. Every sentence earns its place with no redundancy or filler.
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 that there is no output schema and no annotations, the description is somewhat incomplete. It explains the purpose and input but does not describe what the tool returns (e.g., a list of conflicts) or how conflicts are determined beyond the examples. This is a notable gap for a detection tool, but the purpose is clear enough for basic 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 already provides 100% coverage of both parameters with clear descriptions, so baseline is 3. The tool description does not add meaningful parameter-level detail beyond what the schema states, though the examples ('flex' and 'grid') give a sense of expected class_string values.
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 function with a specific verb ('detects'), a specific resource ('conflicting Tailwind utility classes'), and provides concrete examples ('flex' and 'grid'). This distinguishes it from sibling tools like validate_class_string, which likely validates validity rather than conflict detection.
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 gives clear context on when to use the tool: 'Use to prevent silent CSS bugs before generating or reviewing component code.' However, it does not explicitly mention when not to use it or compare it to alternatives, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It transparently discloses the parse-extract-return behavior and gives an output example, which is adequate for a read-only operation. However, it does not mention edge cases like missing files or multiple @theme blocks, so it's not exhaustive.
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 two sentences, direct, and front-loaded with the action verb. Every word earns its place, and the output example is concise.
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?
Given a single parameter and no output schema, the description adequately covers purpose, input, and output format with a concrete example. It lacks error-handling details but is sufficient for a simple, read-only parsing tool.
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 schema already describes the css_path parameter with 100% coverage, so baseline is 3. The description adds the qualifier 'project's root CSS file', which narrows the intended path beyond the schema's generic 'CSS file'. This extra context enhances parameter understanding.
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 parses the project's root CSS file (Tailwind CSS v4) and extracts design tokens from @theme blocks, with a concrete example of the output format. This specific verb+resource combination distinguishes it from the generic sibling 'resolve_theme_tokens'.
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 context (Tailwind v4, @theme blocks) but does not explicitly differentiate from alternatives like 'resolve_theme_tokens' or state when not to use this tool. No when/when-not guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of disclosing behavior. 'Queries' and 'returns all design tokens' imply a read-only operation with an output, which is appropriate for a simple lookup tool. It stops short of explicitly stating no side effects or mentioning error behavior, but the non-mutating nature is reasonably clear.
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, front-loaded with purpose, then use case, then examples. Every sentence contributes meaning without unnecessary repetition or fluff.
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?
The description explains the tool's core purpose and gives examples, but there is no output schema and the description does not describe the return structure. The sibling tool resolve_v4_theme_variables suggests a possible version distinction (v3 vs v4) that is not addressed, leaving some ambiguity about which Tailwind versions are supported.
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 100%, so baseline is 3. The description adds value by providing concrete namespace examples ('colors', 'colors.brand', 'spacing') and clarifies that namespace is a 'dot-path,' which helps the agent understand the parameter semantics beyond the schema.
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 opens with a specific verb and resource: 'Queries the resolved Tailwind CSS theme for a specific namespace and returns all design tokens.' It clearly identifies what the tool does and differentiates it from siblings like get_config_summary by focusing on resolved token values for a named namespace.
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?
Provides explicit usage context: 'Use before generating components to discover what colors, spacing, or font values actually exist in this project.' This is clear guidance on when to invoke the tool, though it does not mention when not to use it or name alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does exceptionally well. It details the return structure (valid/invalid/possibly_valid classes, conflict warnings), discloses the limitation that plugin-generated classes cannot be verified without PostCSS, and lists supported syntax (variants, important, opacity modifiers, negative values, arbitrary values). This is comprehensive behavioral disclosure.
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 two sentences with zero filler. The first sentence packs functionality, output categories, and supported syntax; the second sentence gives a clear directive. Every phrase contributes, and the structure is front-loaded with the core verb and resource.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite lacking an output schema, the description explains the return values and limitations, covers a wide range of class syntax edge cases, and gives a concrete usage scenario. It is complete for a validation tool of this complexity, and no critical behavioral detail appears to be missing.
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 100% for both parameters, so the baseline is 3. The description adds value by explaining the accepted syntax for class_string (variants, important, opacity modifiers, negative values, arbitrary values), which goes beyond the schema's simple 'space-separated' definition. It does not add meaning to config_path, but the overall parameter semantics are enhanced.
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 validates a Tailwind CSS className string against the project's config, and it distinguishes itself from sibling tools by focusing on class string validation rather than config summaries or theme resolution. It also enumerates specific outputs (valid_classes, invalid_classes, possibly_valid_classes, conflict warnings), making the purpose unmistakable.
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?
It explicitly gives a use case: 'Use to catch hallucinated design tokens before writing code.' This provides clear when-to-use context, but it does not explicitly mention when not to use it or direct users to alternatives, which is a minor omission given the single-purpose nature of the tool.
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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