encode-ui

by reuvenaor

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Server Quality Checklist

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Latest release: v0.4.0
Disambiguation5/5
Each tool has a clearly distinct purpose: searching, finding similar, fetching metadata, fetching source, listing groups, listing components, getting install commands, and finding icons. The descriptions explicitly clarify differences (e.g., search_components vs list_components), so there is no ambiguity.
Naming Consistency4/5
Most tools follow a verb_noun pattern (search_components, get_component, list_groups, list_components, find_icons). Minor deviations include find_similar (verb + adjective) and compound nouns like get_component_source and get_install_command, but these are still readable and predictable.
Tool Count5/5
With 8 tools, the set is well-scoped for a component registry server. Each tool covers a distinct aspect of discovery and installation without redundancy, fitting the typical 3-15 tool sweet spot.
Completeness5/5
The tool surface covers the full read/install workflow: searching, browsing by group, enumerating, getting metadata, getting source, getting install commands, and verifying icons. No obvious gaps exist for the server's stated purpose of exploring and installing components.
Average 4.5/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 48 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
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

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as read-only, so the description doesn't need to restate that. It adds useful behavioral context: results are computed from the fetched registry index, and the score is a structural heuristic comparable only within one result set. These details prevent misuse and set expectations beyond what the schema or annotations provide.
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 tightly written sentences, front-loaded with the core purpose and followed by use cases and a caveat. Every sentence adds value, with no redundancy or fluff.
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?
With simple parameters, existing annotations, and an output schema, the description provides enough context: it explains the basis of relatedness, the nature of the score, and when to use the tool. The only minor gap is that it doesn't mention whether the registry index needs to be pre-fetched, but this is a small omission 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.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Both parameters (name and k) have clear descriptions in the schema, so the baseline is 3. The description doesn't add per-parameter semantics, but given the 100% schema coverage, it is not necessary for 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's function: it finds components related to a given one via same group, shared categories, and composition edges. It also frames specific use cases ('what else is like this', finding variants), which distinguishes it from siblings like search_components or get_component.
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 when to use the tool (finding related components and variants) and adds a caveat that scores are structural heuristics, not embedding similarities. However, it does not explicitly name alternatives or state when not to use this tool, so it lacks explicit exclusions but provides sufficient situational 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 only say read-only/open-world. The description adds valuable behavioral details: output is fenced code blocks, multi-file components return ALL sibling files, and it returns text rather than JSON for readability. This goes beyond the annotations and sets correct expectations without contradicting them.
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?
Three sentences, each earning its place: what is returned, why multi-file works, and a caution about size plus output format. No wasted words, front-loaded with purpose.
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?
With no output schema, the description sufficiently explains the return format (fenced code blocks, text) and the notable multi-file behavior. The size check tip provides operational context. It could mention error handling or whether file paths are included, but for a simple read operation it is fairly 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?
The schema already covers both parameters with 100% description coverage (name as slug, part enum with source/demo). The description only loosely references the demo payload and doesn't add parameter-specific semantics beyond what the schema provides, so it stays at the baseline of 3.
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 title 'Get a component's source' and description make clear that this tool returns the TSX source files for a component or its demo example. It also distinguishes itself from sibling get_component by referencing sourceBytes/demoBytes and noting it returns actual code as fenced blocks, not a JSON field.
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 actionable guidance: 'Check sourceBytes/demoBytes on get_component first if size matters' explicitly directs when to use the alternative tool. It also explains why multi-file components return all files (entry alone won't compile), implying a use case for full source retrieval. However, it doesn't explicitly state when NOT to use this tool beyond the size consideration.
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?
Beyond the readOnlyHint annotation, the description discloses that it reports byte size without sending source/demo content, clarifying a potentially surprising behavior. It also lists the metadata fields returned. This adds useful context about what the tool does and doesn't do.
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, first front-loads the core purpose and deliverables, second adds behavioral detail and points to a sibling. No redundancy; every sentence earns its place.
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 a single parameter, an output schema, and readOnly annotation, the description is complete. It covers what the tool returns, notes the size-only behavior, and directs users to the source-fetching sibling, leaving no important gaps.
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 a clear description of the 'name' parameter. The description only reinforces 'by name' without adding new semantics, aligning with the baseline for complete schema coverage.
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: 'Metadata for one component by name' and enumerates specific fields returned (description, group, dependencies, provenance/license, install command). It distinguishes itself from siblings like get_component_source by noting it reports byte size without sending the source.
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?
Explicitly instructs to use get_component_source for actual source: 'fetch those from get_component_source once you know what they cost.' This gives clear when-to-use guidance. However, it doesn't explicitly contrast with search_components or list_components for finding components, though 'by name' implies exact lookup.
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 the readOnlyHint annotation, the description discloses alias resolution with deprecation flags, the output fields (canonical name, import identifier, tag/category context), and the registry's role (no icon components; lucide-react is the icon layer). This adds substantial behavioral context.
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 dense sentences with no filler. It front-loads the core promise, then covers modes, output, and dependency information efficiently.
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?
For a read-only lookup tool, the description covers required mode constraints, output characteristics, alias behavior, and ecosystem context (lucide-react version). No important information appears 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?
The schema already documents all parameters thoroughly (100% coverage). The description adds value by clarifying how the modes interact (at least one required, names wins over query/category) and what query searches (name, tags, categories).
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 finds or verifies lucide-react icon names, with specific modes (query, names, category). The title and description align, and the focus on icon names distinguishes it 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?
Usage is implied through the value proposition 'never guess an icon import' and the description of three modes. However, it does not explicitly state when to use this tool over the sibling tools or provide exclusions.
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 include readOnlyHint=true, so the read-only nature is already known. The description adds valuable behavioral context: it returns full membership (not a sample), includes name/title/description per item, and notes that get_component holds detailed data. This goes beyond annotation declarations without overclaiming.
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, with the core purpose front-loaded in the first sentence. Each subsequent sentence adds distinct value—usage guidance and output summary—without fluff or repetition.
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 (one required param, two total), the description covers what it does, when to use it, and what it returns. The output schema and parameter descriptions fill remaining gaps, so the overall context is 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 100%, so both parameters are fully documented in the schema (group slug and dependencyFree filter). The description does not add new parameter-level semantics beyond the schema's own explanations, 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 clearly states the tool enumerates every component in a group, specifying 'full membership, not a ranked sample.' It also distinguishes itself from search_components by explicitly contrasting with search's top-k results, making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool over alternatives: 'Search always returns its top k, so "list ALL X" and "does the registry have X?" need this instead.' It also points to get_component for details, giving clear usage context.
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?
The description adds valuable behavior beyond the annotations: duplicates are collapsed and unknown names are returned under 'unknown'. Since readOnlyHint and openWorldHint are already provided, this disclosure of edge-case handling is strong and non-contradictory.
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 core action, and no filler. Every word contributes to understanding the tool's behavior.
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, the presence of an output schema, and read-only annotations, the description covers all essential aspects: purpose, bulk behavior, duplicate handling, and unknown-name handling. No significant gaps remain.
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 has 100% coverage with descriptions for the 'names' parameter and its items. The description adds meaning by explaining how the parameter values are processed (duplicates collapse, unknown names grouped), which goes beyond raw schema definitions.
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 produces a single `npx shadcn@latest add` command for the named components. This specific verb+resource combination distinguishes it from sibling tools like search_components or get_component.
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 conveys clear context: use this when you want a bulk install command for specific component names. It does not explicitly name alternatives or exclusions, but the purpose is unambiguous in the context of sibling tools.
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 closed-world semantics. The description adds useful context about the output structure (taxonomy with counts) and how it relates to sibling tools, without contradicting the annotations. It slightly exceeds minimal additions by explaining the semantic content of the result.
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 redundancy. Every clause adds value: the first describes the output, the second directs the user to related tools. The structure is clean and front-loaded with the core purpose.
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 no parameters and an output schema, so the description's coverage of the tool's purpose and usage is sufficient for an agent to select and invoke it correctly. The relationship to sibling tools is clearly articulated, covering all necessary context.
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?
No parameters exist; the schema is empty with 100% coverage. The description doesn't need to explain parameters, and the baseline for zero-param tools is 4. The description adds no param-specific info, but none is needed.
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 identifies the tool as listing the registry taxonomy with per-group component counts, and explicitly distinguishes it from list_components and search_components by explaining how the output is used. The specific resource (registry taxonomy) and scope (component count per group) are stated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly states when to use this tool: to get a slug for filtering search_components or to identify a group to enumerate with list_components. This provides clear alternatives and usage context, going beyond mere statement of purpose.
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 the annotations (readOnlyHint=true, openWorldHint=false), the description reveals key behavioral traits: it's a 'plain substring filter' over an index, drops single characters and splits on punctuation, and clarifies that 'score' is rank-derived and not calibrated while 'cosine' is always null. This adds significant context about matching limitations and output semantics, fully earning a high score.
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 longer than average, but every sentence serves a purpose: purpose, engine mechanics, query quirks, alternative discovery method, install-command note, and absence limitation. It is front-loaded with the core purpose and then expands logically. However, some phrasing could be tightened (e.g., the 'Web engine' sentence packs many ideas), so it's not maximally concise.
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 complexity (6 params, output schema, multiple siblings), the description is remarkably complete. It covers what the tool does, how it works, its limitations, when to use alternatives, and even advises reading a catalog resource for better judgment. The output schema exists, so no need to detail return values; the description handles all critical context.
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 the baseline is 3. The description adds meaning beyond the schema by explaining behavior of the query parameter: 'Terms split on punctuation and single characters are dropped, so "toast?" and "a dialog." behave like the bare words.' It also notes that 'exact names and aliases... work best,' which directly aids parameter usage. Other parameters are already well-described in the schema, so the description provides a modest but valuable increment.
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+resource+scope: 'Find React components in the encode-ui registry by name, alias, or keyword.' This clearly distinguishes it from siblings like list_components (which lists everything) and get_component (which fetches a single component). The mention of alternatives in the latter half further reinforces differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool vs. alternatives: 'For discovery and planning, read the catalog resource... once and judge the descriptions yourself' and 'A filter cannot prove absence: for "find ALL X" or "does the registry have X?" use list_groups + list_components.' It also warns about exact-name matching versus loose behavior phrasing, giving clear context for effective use.
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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