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Jon2G

cursor_admin_mcp

by Jon2G
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TypeScript
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Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.2.0

  • Disambiguation5/5

    The two tools are clearly distinguished by target platform: run_as_admin for Windows and run_as_root for Linux/macOS. There is no overlap or ambiguity in their purposes.

    Naming Consistency5/5

    Both tools follow a consistent 'run_as_<privilege>' naming pattern. The verb 'run' and the privilege level are clearly indicated, making the pattern predictable.

    Tool Count4/5

    The server has exactly two tools, covering elevated command execution on all major OS platforms. While minimal, the count is appropriate for the narrow domain of admin command execution.

    Completeness5/5

    The tool set covers the entire domain of running commands with elevated privileges across all supported OS platforms (Windows, Linux, macOS). No obvious gaps exist for the stated purpose.

  • Average 3.6/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 2 commits in the last 12 weeks
    • Last stable release on
    • 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.

  • 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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  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
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  ]
}

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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?

    Discloses that the tool requires visual user approval and sudo authentication, indicating it is not fully automated. Without annotations, it carries the burden of transparency, but lacks details on destructiveness, error handling, or return behavior.

    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 a single, concise sentence that includes both platform and execution context. It is front-loaded and efficient, though could be slightly more structured.

    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?

    Given a single required parameter and no output schema, the description adequately covers the tool's purpose and execution context. However, it lacks information on what the tool returns or error scenarios, which would be valuable for completeness.

    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 covers 100% of parameters with a description. The tool description adds context about approval and sudo, but does not add semantic detail beyond what the schema already provides for the 'command' parameter.

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

    Purpose4/5

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

    The description clearly states the tool executes a bash command as root on Linux/macOS, which is specific and distinct from typical commands. However, it does not explicitly differentiate from the sibling tool 'run_as_admin', which could cause ambiguity.

    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?

    Mentions visual user approval and sudo authentication as prerequisites, but does not specify when to use this tool versus run_as_admin or provide explicit exclusions or alternatives.

    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?

    Discloses platform restriction, admin privilege requirement, and user approval step. However, with no annotations provided, the description does not cover failure behaviors, return value, or execution model (synchronous/asynchronous), which are relevant for a mutation 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?

    Single sentence, front-loaded with key constraint (Windows only), no redundant information. Every word adds value.

    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?

    For a single-parameter tool with no annotations and no output schema, the description covers platform, privilege, and approval but omits execution details (e.g., success/failure, output format). Could be more 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 covers 100% of parameters with description matching command semantics. Description adds no additional meaning beyond what the schema already provides.

    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 it executes a PowerShell command with Administrator privileges, specifies Windows-only and visual user approval. Distinguishes from sibling run_as_root by platform and privilege model.

    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?

    Implicitly indicates Windows-only usage and differentiation from run_as_root, but lacks explicit when-to-use, when-not-to-use, or alternative guidance. No mention of scenarios requiring admin vs standard execution.

    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.

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