mcp-interactive-ui-server

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool generates a distinct UI component type (chart, dashboard, form, table, wizard, markdown), with no overlapping purposes or ambiguous boundaries.
Naming Consistency4/5
Five tools follow the 'create_' prefix pattern, but 'render_markdown' breaks this by using 'render_', causing a minor inconsistency.
Tool Count5/5
Six tools cover the essential interactive UI components well, without being overly numerous or insufficient for the server's stated purpose.
Completeness4/5
The set covers major UI components (charts, forms, tables, dashboards, wizards, enhanced markdown), but lacks more specialized components like modals or cards, though these are not essential.
Average 4.1/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 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 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.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
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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?
No annotations are provided, so the description bears full responsibility. It discloses that it creates a JSON schema and lists supported field types, but lacks details on side effects, idempotency, validation, or error handling. Adequate but not extensive.
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 well-structured with a summary, supported types, field requirements, and an Args section. It is front-loaded and logically organized, though a bit verbose in places.
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 the tool's complexity (5 params, 2 required, output schema present), the description covers purpose, parameter details, and output type. It does not need to explain return values due to output schema. Minor omission of validation behavior but overall complete enough for correct use.
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 description coverage is 0%, so the description compensates by detailing the fields parameter's item structure, required minima, and optional parameters like layout with allowed values. Adds significant meaning beyond the schema's type-only 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 it generates a JSON form definition with various field types, and lists supported field types. It distinguishes itself from sibling tools like create_chart, create_dashboard, etc., by focusing specifically on form creation.
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 creating interactive form UIs but does not explicitly state when to use this tool versus alternatives like create_wizard. No exclusions or when-not-to-use guidance provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It describes the tool's behavior (creating a wizard definition with steps and validation) and mentions the return type (JSON string). However, it does not disclose side effects, authentication needs, or whether the wizard is a schema or interactive, which is partially covered by the return statement.
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 moderately concise, with a clear structure: overview, step definition format, validation rules, parameter list, and return type. Some redundancy exists (first two sentences both mention multi-step and validation), but overall it is well-organized and not overly verbose.
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 the tool's complexity (4 parameters, nested step objects, output schema), the description covers all essentials: parameter details, step/validation examples, and return type. It explains how clients use the output, making it complete for an agent to understand and 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 description coverage is 0%, meaning the input schema properties lack descriptions. The description compensates by explaining each parameter (steps, title, allow_skip, show_progress) and providing details on step structure and validation rules, adding significant value beyond 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 clearly states it generates a multi-step form wizard with validation, distinguishing it from siblings like create_form (single form) and create_chart. It specifies the verb and resource, and details about step definitions and validation rules.
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 multi-step wizards but does not explicitly say when to use this tool versus alternatives like create_form. No 'when not to use' or comparative guidance, though the context of siblings indirectly suggests distinction.
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 full burden. It states the output (JSON string with dashboard schema) and widget requirements, but does not explicitly confirm whether the tool has side effects (e.g., persistence) or is purely generative. The verb 'erstellt' is ambiguous, though the return format suggests no side effects.
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 well-structured with clear sections (supported types, example, args) and is informative without being overly verbose. Minor redundancy could be trimmed, but it remains concise and front-loaded.
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?
Overall, the description covers the tool's purpose, parameters, return type, and usage context adequately for an AI agent. It could be improved by clarifying the precise output schema format or persistence behavior, but it is sufficient given the tool's complexity.
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?
The description adds significant value beyond the input schema, including constraints (columns 1-6), meaning of refresh_interval (seconds with 0 = no refresh), and detailed explanation of widgets with supported types and example structure. This compensates for the schema's minimal metadata.
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 generates a dashboard layout with configurable grid layout and widget types. It lists supported widget types and provides an example, which distinguishes it from sibling tools like create_chart or create_table that serve different purposes.
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 creating interactive dashboards but does not explicitly compare to siblings or provide when-to-use/alternatives guidance. The context is implied by the tool's name and description, but no exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries full burden. It mentions the return format (JSON string) and features (sorting, filtering, pagination) but lacks disclosure of limitations, validation, or side effects. It is adequate but not thorough.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, using a one-line summary followed by a structured args list. Every sentence adds value, with no redundancy, making it efficient for an AI agent to parse.
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 the tool's purpose, parameters, and return type, but misses constraints like header-row length matching and error handling. Given the simplicity of a table creation tool, it is mostly complete but could include more edge-case details.
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?
The description includes a full args section explaining each parameter's meaning and structure, compensating for the 0% schema description coverage. It adds value beyond the schema's names and types, providing clear semantics for all 6 parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it generates an interactive table definition with sorting and filtering, using a specific verb and resource. It distinguishes itself from sibling tools like create_chart and create_form, 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 Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not explicitly state when to use this tool versus alternatives. While the purpose is clear, it provides no guidance on exclusions or alternative contexts, leaving the agent to infer from sibling names.
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?
The description explains that the tool creates a chart schema and returns a JSON string, with no side effects or destructive actions mentioned. Without annotations, it adequately discloses the return format and data examples.
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 well-structured with sections for supported types, data formats, args, and returns. Slightly lengthy but each part is informative, earning 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?
Given an output schema exists (mentioned), the description provides sufficient context: chart types, data formats, and parameter details. No gaps remain for a typical usage scenario.
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?
With 0% schema description coverage, the description fully compensates by listing all parameters, their meanings, and detailed data format examples for each chart type. This adds essential meaning beyond the bare 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 clearly states the tool generates a chart definition with specific types (bar, line, pie, etc.) and provides data format examples. It distinguishes from sibling tools like create_dashboard or create_form, which serve different purposes.
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 the tool is for creating chart definitions but does not explicitly provide when-to-use or when-not-to-use guidance. No alternative tools are mentioned for comparison, though sibling names suggest distinct purposes.
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 explains the embedding mechanism via placeholders, lists supported element types with JSON structures, and states the return type (JSON string with enhanced Markdown schema). It lacks details on error handling or limits but is fairly transparent for a rendering tool.
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 well-structured: a concise header, explanatory paragraphs, a list of supported types with JSON examples, and an 'Args:' section. Every sentence adds value and there is no redundancy.
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 the complexity (3 parameters, one required, output schema exists), the description covers the main aspects: purpose, parameters, supported elements, and output format. It omits constraints like maximum input length or error behavior, but is nearly complete for an agent to use the tool 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?
Schema coverage is 0%, so the description fully compensates. It provides an 'Args:' section describing each parameter (content, interactive_elements, theme) with types, defaults, and concrete JSON examples for each element type. This adds significant meaning beyond the schema's 'title' and 'type' fields.
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 'Erweitertes Markdown mit eingebetteten interaktiven Elementen', specifying the tool renders Markdown with interactive components. It distinguishes from sibling tools like create_chart, create_dashboard, etc., which are about creating different artifacts.
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 rendering interactive Markdown but does not explicitly state when to use this tool versus alternatives or provide exclusion criteria. No comparative guidance is given.
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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