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IqbalHere

Visual Loop MCP Server

by IqbalHere
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TypeScript
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Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of ambiguity or confusion with other tools.

    Naming Consistency5/5

    With a single tool, naming consistency is not a concern; the name 'execute_and_render' clearly describes its function.

    Tool Count3/5

    One tool is on the lower end of reasonable count, but it serves a specific combined purpose of execution and rendering, which may justify the minimal surface.

    Completeness3/5

    The single tool covers both code execution and visual rendering, but lacks separate tools for execution-only or rendering-only tasks, limiting flexibility.

  • Average 4.1/5 across 1 of 1 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 1 commit 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.

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  • 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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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

  • 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 mentions the process (Codebox execution, Playwright rendering) and outputs (console output, screenshot), but does not disclose potential side effects, execution limits, error behavior, or authentication requirements. This leaves gaps for an AI agent.

    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 core functionality in the first sentence. Every word is purposeful with no fluff. It is optimally sized for an AI agent to quickly grasp the tool.

    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 schema covers all parameters, and no output schema exists. However, the description does not specify the format of the console output or screenshot (e.g., base64, plain text). It also omits constraints like timeouts or size limits, which are relevant for code execution tools. This information would be useful for correct invocation.

    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 each parameter described in the schema. The description adds no additional parameter details beyond what is already in the schema. According to the guidelines, baseline is 3, and no extra value is provided.

    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 executes source code via Codebox (Judge0) for logic validation, renders it as HTML via Playwright, and returns console output and a screenshot. The verb 'execute and render' and the specific resource (Codebox and Playwright) make the purpose unambiguous. Without sibling tools, differentiation is not needed.

    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 explicitly says 'Use this to write full-stack UI code and instantly verify both its logic and visual appearance,' providing a clear usage context. There are no sibling tools, so no alternatives to exclude, making the guidelines sufficient.

    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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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.

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