Skip to main content
Glama
ubbcou

system-command-mcp

by ubbcou
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Local

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    system_environment provides information about the execution environment and registered program names, while system_exec actually executes a program. Their purposes are completely distinct with no overlap, making selection unambiguous.

    Naming Consistency5/5

    Both tools follow a consistent 'system_' prefix pattern in snake_case. Although 'environment' is a noun and 'exec' is a verb, the uniform prefix and clear naming make the pattern predictable.

    Tool Count3/5

    With only two tools, the server feels minimal and borderline thin for even a focused command-execution utility. The count is not egregiously low, but it leaves little room for additional useful operations.

    Completeness4/5

    The two tools cover the core needs of querying the environment and executing registered programs. Minor gaps exist, such as no capability to manage program registrations or handle interactive sessions, but these are likely beyond the server's intended scope.

  • Average 4.5/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 22 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is failing

  • 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

  • Behavior3/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations, the description must carry behavioral transparency. It conveys the tool is authoritative and not a probing tool, but it does not explicitly state whether it has side effects, what exactly 'registered logical program names' means, or potential output format limitations, leaving some ambiguity.

    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 with no redundancy. The first sentence states purpose, the second provides usage guidance—concise and well-structured.

    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?

    For a simple tool with no parameters and no output schema, the description covers purpose, usage, and gives a warning about probing. It's fairly complete, though 'authoritative system execution environment' could be more precisely defined.

    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 tool has zero parameters, and the schema is empty. The description doesn't need to explain parameters, and the baseline for 0 params is 4. It adds context about the tool's output scope but not parameter details.

    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 returns the 'authoritative system execution environment and registered logical program names', which is specific and differentiated from the sibling tool system_exec by explicitly noting program availability is covered there.

    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 tells when not to use this tool: 'do not probe with which, where, command -v, or Get-Command', and directs users to system_exec for program availability, providing clear alternatives and context.

    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 provided, the description carries the full burden. It discloses key behaviors: no shell support, no quoting, strict program name whitelist, and cross-platform abstraction. However, it does not mention return value/exit code handling or error behavior, which would be valuable for an execution 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 three sentences, front-loaded with the core purpose, followed by necessary constraints. Every sentence earns its place, with no redundancy or filler.

    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 tool has no output schema, so the description should clarify what happens after execution (stdout/stderr, exit code, or side effects). It does not, which leaves a notable gap. Otherwise, it covers the main execution constraints and usage context well.

    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 meaningful context: program names must come from the enum, args are passed literally without shell escaping, and cwd is relative to the configured root. This supplements the schema descriptions with practical usage constraints.

    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: 'Execute one registered system program directly through a cross-platform program-and-arguments interface.' It identifies the specific verb (execute), resource (registered system program), and interface style, distinguishing it from the sibling system_environment which concerns environment information.

    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 usage rules: 'Use only program names in the enum,' 'Pass arguments separately without shell quoting,' and lists unsupported constructs (shell operators, pipelines, redirects, command substitution, environment expansion). It also warns against probing or guessing alternative executable names, giving clear boundaries for when and how to use the tool.

    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

system-command-mcp MCP server

Copy to your README.md:

Score Badge

system-command-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/ubbcou/system-command-mcp'

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