Skip to main content
Glama
markgregg

Low-Code UI MCP Server

by markgregg
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

50%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools, as there are no other tools to compare it to. The tool's purpose is clearly defined and stands alone without any confusion.

    Naming Consistency5/5

    Since there is only a single tool, it inherently maintains consistency with itself. The naming follows a clear verb_noun pattern (suggest_ui_instructions), which is straightforward and predictable.

    Tool Count2/5

    A single tool is too few for a server named 'Low-Code UI MCP Server', which implies a broader domain of UI-related operations. This minimal set feels thin and inadequate for covering potential needs like creating, updating, or rendering UI components.

    Completeness2/5

    The tool surface is severely incomplete for a UI-focused server, as it only offers suggestion capabilities without any tools for actual creation, modification, or execution of UI elements. This leaves significant gaps that will likely cause agent failures in handling full UI workflows.

  • Average 2.3/5 across 1 of 1 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

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

    MCP servers without a LICENSE cannot be installed.

  • 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.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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?

    Zero annotations provided, so the description carries full disclosure burden. It fails to clarify whether this is a pure function (likely), what 'instructions' entail (schema? config?), or how the parameters influence UI selection (carousel vs table thresholds). The mention of '@workspace/ui renderer' hints at the domain but doesn't explain behavioral traits.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness2/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    While the single sentence is front-loaded, it is inappropriately concise given the tool's complexity (5 parameters, nested objects, output schema). The brevity creates underspecification; the description wastes its opportunity to explain the tuning parameters or output format.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Inadequate for a tool with 5 parameters, 0% schema coverage, and no annotations. The description omits the logic governing UI component selection (when carousel vs table vs other), the nature of the returned instructions, and the purpose of the threshold parameters. Even with an output schema, the input semantics require elaboration.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    With 0% schema description coverage, the description must compensate for five undocumented parameters. It only implicitly references the 'data' parameter via 'JSON-like object', completely omitting the four configuration parameters (carousel_max_items, table_min_items, etc.) which clearly control rendering heuristics. Barely above tautology.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose3/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description states the core action ('Suggest') and target ('@workspace/ui renderer instructions'), but 'instructions' remains vague and '@workspace/ui' assumes domain knowledge without explanation. It adequately identifies the input as a 'JSON-like object' but lacks specificity about what the tool actually produces.

    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 provided on when to use this versus manual UI configuration or other rendering approaches. No mention of prerequisites (e.g., whether the data needs specific structures) or when the suggestions are inappropriate.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

mcp MCP server

Copy to your README.md:

Score Badge

mcp MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/markgregg/mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server