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Pratyay

MacOS Resource Monitor MCP Server

by Pratyay
OverviewSchemaRelated ServersScoreDiscussions
Python
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.2.0

  • Disambiguation4/5

    Tools are generally distinct: one lists processes by category, another identifies intensive ones, and the third gives a system overview. However, there is slight overlap between the first and second, as both deal with processes, but their purposes differ (listing vs. highlighting intensive ones).

    Naming Consistency5/5

    All tool names follow a consistent pattern: verb_noun (get_*) with descriptive suffixes. The snake_case style is uniform, making the set predictable and easy to use.

    Tool Count5/5

    Three tools is well-scoped for a resource monitor. Each tool serves a clear, non-redundant purpose, covering per-category listings, intensive process detection, and system overview without being excessive.

    Completeness4/5

    The tool set covers the core monitoring needs: CPU, memory, network, disk, and performance analysis. Minor gaps exist, such as no tool for querying a single process by PID or filtering across all categories in one call, but these are not critical for the stated purpose.

  • Average 4/5 across 3 of 3 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

  • Behavior2/5

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

    No annotations are provided, so the description carries full burden. It only states what it returns (JSON with metrics) but does not disclose behavioral traits like read-only nature, permission requirements, or potential performance impact. Lacks depth.

    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-loads the main purpose, and includes a return type. Every sentence adds value; no wasted words. Efficient and clear.

    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 zero parameters and an output schema present, the description adequately explains what the tool does and returns. However, it lacks usage context (when to use vs siblings) and behavioral notes, which would improve completeness for a simple tool.

    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?

    No parameters are present (schema coverage 100%), so the baseline is 4. The description does not need to add parameter meaning, but it correctly implies no input needed. No additional value beyond schema is required.

    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 'Get comprehensive system overview' and lists specific metrics (CPU, memory, disk, network) and purpose (identify bottlenecks). It differentiates from sibling tools like get_processes_by_category by being a broad overview, not process-specific.

    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 use for system health checks but does not explicitly state when to use this tool versus alternatives or provide any exclusion criteria. No guidance on prerequisites or context.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior2/5

    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 only mentions the return type (string) and potential analysis, lacking details on performance impact, privileges, or whether it's a snapshot.

    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 main action, and contains no unnecessary information.

    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 no-parameter tool with an output schema, the description covers the basic purpose and return. Minor gaps: no format specifics or examples, and sibling differentiation could be stronger.

    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?

    There are no parameters, and schema coverage is 100%. The description adds no parameter semantics because none exist, achieving the baseline of 4.

    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 it identifies resource-intensive processes on macOS across CPU, memory, and network, differentiating it from sibling tools like 'get_processes_by_category' and 'get_system_overview'.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The tool's purpose is clear, but it does not explicitly state when to use it versus alternatives. The sibling names imply differentiation, but no direct guidance is provided.

    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?

    No annotations are provided, so the description fully carries the burden of behavioral disclosure. It accurately describes the operation as read-only (getting processes), includes pagination (page, page_size with defaults and max), sorting (sort_by, sort_order), and return format (JSON string). It does not mention destructive actions, auth, or rate limits, which is acceptable for a read tool.

    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 structured with Args and Returns, and the main purpose is front-loaded. It is somewhat long but every sentence adds value (defaults, categories, sort details). Minor redundancy: 'JSON string containing paginated and sorted process information' could be condensed, but overall efficient.

    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 5 parameters (1 required), no annotations, and an existing output schema, the description covers all parameter semantics, defaults, return format, and behavioral constraints (max page size). It lacks edge cases (e.g., empty results, error handling), but completeness is high for a paginated list tool.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 0%, so the description provides all parameter details. It explains process_type values ('cpu', 'memory', 'network'), page defaults, page_size max, sort_by options with per-category specifics, and sort_order choices. This adds significant meaning beyond the schema's titles and types.

    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 'Get all processes filtered by category (cpu, memory, network) with pagination and sorting support.' It uses a specific verb ('Get') and resource ('processes'), and the categories distinguish it from siblings like 'get_resource_intensive_processes' and 'get_system_overview'.

    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 (processes by category) but does not explicitly state when to use this tool versus siblings. It mentions 'filtered by category' but provides no guidance on alternatives (e.g., 'use get_resource_intensive_processes for high-usage processes'). Sibling tools are listed but not compared.

    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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  • Evaluate tool definition quality.

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