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DRITI2906

MCP System Info Server

by DRITI2906
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'get_sysinfo' has a clearly defined and distinct purpose of retrieving comprehensive system information.

    Naming Consistency5/5

    Since there is only one tool, naming consistency is inherently perfect. The tool name 'get_sysinfo' follows a clear verb_noun pattern, and there are no other tools to create inconsistency.

    Tool Count2/5

    A single tool is generally too few for a server's purpose, as it limits functionality and scope. For a system info server, one tool might be insufficient for covering potential needs like monitoring specific components or historical data, making it feel thin and under-scoped.

    Completeness2/5

    The tool provides comprehensive system information in one call, but there are significant gaps for a system info domain. Missing operations include monitoring changes over time, querying specific subsystems individually, or performing actions like alerts or logs, which limits agent workflows and could lead to failures in dynamic scenarios.

  • Average 3.4/5 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

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  • This repository includes a README.md file.

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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 carries the full burden. It discloses the return format in detail (system, CPU, memory, disk, GPU information), which is valuable behavioral context. However, it doesn't mention potential limitations like performance impact, permissions needed, or error conditions.

    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 and front-loaded with the main purpose, followed by a bulleted list of return details. It's appropriately sized with no redundant information, though it could be slightly more concise by integrating the bullet points into a single sentence.

    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 (system monitoring), no annotations, and an output schema present, the description provides good completeness by detailing the return values. It covers key aspects like OS, CPU, memory, disk, and GPU, but could improve by mentioning data freshness or update frequency.

    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 input schema has 0 parameters with 100% coverage, so the baseline is 4. The description appropriately doesn't discuss parameters, focusing instead on the output, which aligns with the tool's no-parameter design.

    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's purpose with the verb 'Get' and resource 'comprehensive system information', making it immediately understandable. However, it doesn't distinguish from sibling tools since there are none, so it cannot achieve a perfect score of 5 for sibling differentiation.

    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?

    The description provides no guidance on when to use this tool versus alternatives, prerequisites, or specific contexts. It simply states what the tool does without any usage instructions or exclusions.

    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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