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allenbijo

MCP System Info Server

by allenbijo
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

67%
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 tool 'get_sysinfo' has a clear and distinct purpose of retrieving comprehensive system information, making it impossible for an agent to misselect among tools.

    Naming Consistency5/5

    Since there is only one tool, it inherently follows a consistent naming pattern. The name 'get_sysinfo' uses a verb_noun format that is clear and predictable, and there are no other tools to compare it against for inconsistency.

    Tool Count2/5

    A single tool is too few for a server named 'MCP System Info Server', which suggests a broader scope for system information retrieval. While 'get_sysinfo' provides comprehensive data, the lack of additional tools (e.g., for specific components like CPU, memory, or disk details) makes the set feel thin and underdeveloped for the domain.

    Completeness2/5

    The tool 'get_sysinfo' covers a wide range of system information in one call, but the server's purpose implies potential for more granular operations. There are significant gaps, such as no tools for querying specific system metrics separately (e.g., get_cpu_info, get_memory_usage) or performing actions like monitoring changes, which limits agent flexibility and could lead to inefficiencies.

  • Average 3.9/5 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

  • This repository is licensed under MIT License.

  • 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 clearly describes the tool's read-only behavior and the types of information returned, but lacks details on performance, rate limits, or error handling. It adequately covers the core behavior without rich contextual disclosures.

    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 categorized details. It is appropriately sized for the tool's complexity, though some redundancy in listing information types could be slightly condensed.

    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 simplicity (0 parameters, no output schema, no annotations), the description provides complete context on what information is retrieved. It adequately compensates for the lack of structured fields, though it doesn't specify output format or potential errors.

    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 no parameter documentation is needed. The description appropriately focuses on the tool's purpose and output, not parameters, meeting the baseline for zero-parameter tools.

    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 specific action ('Get') and resource ('current system information'), with detailed breakdowns of what information is included (System, Node name, CPU, Memory, Disk). It effectively distinguishes this tool's comprehensive scope from any hypothetical alternatives.

    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?

    The description implies usage for retrieving system information, but provides no explicit guidance on when to use this tool versus alternatives (e.g., specific vs. partial system info tools). Since there are no sibling tools, this is adequate but lacks explicit context 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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