Skip to main content
Glama
Pratyay

MacOS Resource Monitor MCP Server

by Pratyay
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
get_resource_intensive_processesA

Identify resource-intensive processes on macOS across CPU, memory, and network.

Returns: A string containing information about resource-intensive processes, which can be analyzed to provide optimization suggestions.

get_processes_by_categoryA

Get all processes filtered by category (cpu, memory, network) with pagination and sorting support.

Args: process_type: Type of processes to retrieve ('cpu', 'memory', or 'network') page: Page number (starting from 1, default: 1)
page_size: Number of processes per page (default: 10, max: 100) sort_by: Sort field - 'auto' (default metric), 'pid', 'command', or metric-specific fields CPU: 'auto'/'cpu_percent', 'pid', 'command' Memory: 'auto'/'memory_percent', 'resident_memory_kb', 'pid', 'command'
Network: 'auto'/'network_connections', 'pid', 'command' sort_order: Sort direction - 'desc' (default) or 'asc'

Returns: JSON string containing paginated and sorted process information for the specified category

get_system_overviewA

Get comprehensive system overview with aggregate statistics similar to Activity Monitor. Provides CPU, memory, disk, network statistics, and performance analysis to help identify bottlenecks and optimization opportunities.

Returns: JSON string containing system overview with performance metrics and analysis

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.1/5.0

Scored across 3 tools

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.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources