color-engine

by aimsise

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Latest release: v1.0.1
Disambiguation5/5
Each tool has a clearly distinct purpose: parsing, converting, contrast calculation, gamut mapping, ramp generation, and solving for contrast. Even where tools overlap conceptually (e.g., contrast vs solve_for_contrast), the boundaries are clear and descriptions eliminate ambiguity.
Naming Consistency4/5
Most names follow a verb_noun pattern (parse_color, convert_color, generate_ramp) or a verb-like imperative (contrast, solve_for_contrast). The name 'gamut_map' deviates from a pure verb_noun pattern ('map_gamut' would be more consistent), but all names are lowercase with underscores and generally readable.
Tool Count5/5
Six tools is well within the ideal 3-15 range and appropriate for a color utilities server. Each tool covers a distinct operation without unnecessary redundancy, making the set feel compact and well-scoped.
Completeness4/5
The tool surface covers parsing, conversion, contrast (including solving for contrast), gamut mapping, and ramp generation—a solid set for color manipulation. Minor gaps exist (e.g., no direct color mixing or component adjustment tool), but these are not essential for the domain and agents can work around them using existing tools.
Average 4.2/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 16 commits in the last 12 weeks
- Last stable release on June 11, 2026
- 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.
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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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds the specific output schema (hex, rgb, oklch, gamut info), which is useful context beyond the 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 a single, front-loaded sentence that states the action and the expected output with no filler or redundancy.
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?
The tool has a single parameter, a comprehensive output schema, and rich annotations. The description adequately covers the purpose and return format; no further details are needed for this low-complexity 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?
Input schema has 100% description coverage, documenting the 'input' parameter with examples. The description does not add additional parameter meaning beyond what the schema provides, so 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 'parse' with a clear resource ('CSS color string') and explicitly lists the return information (hex, rgb, oklch, gamut info), distinguishing it from sibling tools like convert_color which would imply format conversion.
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 alternatives such as convert_color or gamut_map. The description implies a use case but does not state exclusions or preferred contexts.
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?
Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the safety profile is well covered. The description adds the 'canonical' normalization detail but does not discuss error handling, alpha preservation, or other edge-case behavior; nevertheless, the output schema and annotations make this adequate.
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?
A single, front-loaded sentence immediately states the verb, object, and available output formats. There is no filler or redundant explanation, 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.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity, rich annotations, full input schema coverage, and the existence of an output schema, the description is complete enough for an agent to select and invoke the tool correctly. It specifies the input type, output options, and the conversion intent without needing to explain return values.
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% and both parameters (input and to) are described with examples and enum values. The description merely restates the target formats from the enum and adds no additional parameter-level meaning, so the baseline score 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 uses a specific verb ('Convert') and resource ('CSS color string') and enumerates the four canonical output formats (hex, rgb, hsl, oklch), clearly defining the tool's function. It implicitly distinguishes the tool from siblings like parse_color or contrast by focusing on producing a format string.
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: this tool handles converting a CSS color string into a chosen canonical format. However, it does not explicitly mention when not to use it or name alternative sibling tools, so it lacks full exclusion guidance.
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?
Annotations already signal read-only, idempotent, non-destructive behavior. The description adds meaningful behavioral context beyond these hints by explaining the transformation method (OKLCH chroma reduction), the return format (nearest hex, raw OKLCH, clamped flag), and what the clamped flag indicates. No contradictions with 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?
Two sentences, front-loaded with the action word 'Map', and no filler. Each sentence provides essential information: the operation and the output details. This is an exemplar of concise, structured documentation.
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?
The tool is simple with a single parameter and an output schema. The description fully covers the purpose, algorithm, and key output flag, making it self-sufficient for an agent to select and invoke the tool correctly without further clarifications.
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 for the only parameter, including a description and example. The tool description does not add additional semantic meaning about the parameter beyond what the schema states, 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 uses a specific verb ('Map') and clearly identifies the resource ('any CSS color string') and the target ('the sRGB gamut'). It also distinguishes itself from sibling tools by naming the algorithm ('perceptual OKLCH chroma reduction') and the unique output components (hex, OKLCH, clamped flag).
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 when to use the tool: whenever a CSS color string needs to be mapped to the sRGB gamut. However, it does not explicitly mention when not to use it or suggest alternatives like convert_color or parse_color, leaving the usage context somewhat implicit.
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?
Annotations already declare read-only and idempotent behavior. The description adds value by specifying the ordered light-to-dark output, included OKLCH components, WCAG contrast ratios, in-gamut flag, and optional token emission, giving a transparent view of the result structure.
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 main purpose, and includes detailed output specifications without unnecessary verbosity.
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?
Given the 7-parameter tool with an output schema, the description comprehensively covers the output format, ordering, optional design token formats, and computed metrics. No significant context is missing.
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 baseline is 3. The description mentions tokenFormat and base color but does not add meaning beyond the schema's parameter descriptions. No gaps in parameter documentation exist.
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?
Description clearly states 'Generate a tint-to-shade color ramp from a base CSS color' with a specific verb and resource, differentiating it from sibling tools like parse_color and convert_color.
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 intended use case is clear—generating a color ramp from a base color—and the description implies this scope. However, it does not explicitly mention when not to use the tool or name alternatives, so it falls short of full usage guidance.
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?
Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds context about the 'fully opaque' requirement and order sensitivity (text 'a' over background 'b'), which is useful and not contradicted by 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?
Two concise sentences, front-loaded with the primary function and immediately followed by the optional modifier. Every word earns its place with no redundant details.
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?
The tool is simple (3 params, all documented) and has an output schema. The description covers the core behavior, optional flag, and input constraints. It is fully complete 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.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so parameters are already documented. The description enhances this by clarifying the directional relationship ('text `a` over background `b`') and the meaning of the optional `apca` flag, which goes beyond the schema descriptions.
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 it computes the WCAG 2.1 contrast ratio between two CSS colors and returns WCAG tier flags. It also mentions the optional APCA computation, which distinguishes it from siblings like parse_color or solve_for_contrast.
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 clear context: it computes contrast between a text color and background color, with optional APCA. It doesn't explicitly exclude alternatives like solve_for_contrast, but the purpose is unambiguous and an agent can infer when to use it. No exclusions or alternative mentions are given.
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?
Beyond annotations (read-only, idempotent, non-destructive), the description discloses the search strategy ('Binary-searches OKLCH lightness holding hue/chroma fixed'), the return behavior (nearest-compliant hex plus achieved ratio), mode-dependent shapes, and error conditions (PARSE_FAILED, ALPHA_UNSUPPORTED). It also reveals subtle behavioral traps like hue without chroma having no visible effect on achromatic backgrounds. This is rich, non-obvious information that complements annotations.
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 long but densely informative—every sentence contributes (purpose, algorithm, modes, errors, caveats). It would benefit from structural formatting (e.g., separate paragraphs or bullets) to improve scannability, but it avoids fluff and redundancy relative to its complexity.
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?
With an output schema and exhaustive parameter documentation, the description still adds necessary context: it explains the response shape differences, the all-optional superset limitation, error conditions, and the key algorithmic behavior. For a tool with this many parameters and edge cases, the description is fully sufficient for an agent to select and invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Although schema coverage is 100%, the description adds significant meaning beyond field names and types. It explains the interaction between `hue` and `chroma`: 'Giving hue WITHOUT chroma does not add saturation: chroma then defaults to the background's own chroma...' and the reverse case. It also clarifies precedence for `target` vs `targets` and the default for `prefer`. These are semantics not derivable from the schema alone.
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 precise verb+resource: 'Find a foreground color that meets one or more WCAG 2.1 contrast targets against a background.' This clearly distinguishes the tool from siblings like `contrast` (which computes a ratio) and `generate_ramp` (which generates a ramp). The tool's purpose as a solver is unambiguous.
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 for parameter usage: 'Pass `target` for one target or `targets` for several; `prefer` selects lighter/darker/either' and explains precedence ('targets takes precedence'). It also warns against translucent backgrounds and instructs to composite first. However, it does not explicitly name alternative tools for scenarios where this tool is not the right choice, so it falls 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.

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