MCP (Model Context Protocol) server

Glances can expose its system monitoring data through a Model Context Protocol (MCP) server, allowing AI assistants (Claude, Cursor, VS Code Copilot, …) to query real-time metrics and generate structured analyses directly from their chat interface.

The MCP server is mounted alongside the standard RESTful API when the web server is started with the --enable-mcp flag. It uses Server-Sent Events (SSE) as its transport layer, which means any MCP-compatible client that supports SSE can connect to it.

Requirements

The mcp Python package must be installed:

pip install 'glances[mcp]'

Start the MCP server

Add --enable-mcp to any Glances web-server command:

glances -w --enable-mcp

The MCP server is then reachable at http://localhost:61208/mcp.

The SSE endpoint used by MCP clients is:

http://localhost:61208/mcp/sse

To change the mount path (default: /mcp):

glances -w --enable-mcp --mcp-path /monitoring/mcp

Authentication

The MCP endpoint inherits the authentication policy of the web server. When Glances is started with --password, every MCP request must carry valid credentials — either HTTP Basic Auth or a JWT Bearer token (see Restful/JSON API documentation for how to obtain a JWT token).

When no password is configured the MCP endpoint is open.

Resources

MCP resources are read-only data sources that a client can list and read.

Static resources

URI	Description
`glances://plugins`	JSON list of all active plugin names
`glances://stats`	JSON object of all plugins’ current statistics
`glances://limits`	JSON object of alert thresholds for all plugins

Resource templates (parameterised)

URI template	Description
`glances://stats/{plugin}`	Current statistics for one plugin
`glances://stats/{plugin}/history`	Historical time-series for one plugin
`glances://limits/{plugin}`	Alert thresholds for one plugin

Replace {plugin} with any name returned by glances://plugins (e.g. cpu, mem, network, processlist, …).

Prompts

MCP prompts are pre-built analysis templates that embed live Glances data into a system prompt ready to be sent to an LLM.

Prompt name	Description	Parameters
`system_health_summary`	Overall health report: CPU, memory, swap, load, filesystems, network	(none)
`alert_analysis`	Analysis of active alerts with remediation steps	`level` (`warning` / `critical` / `all`)
`top_processes_report`	Report on the most CPU-intensive processes	`nb` (integer, default `10`)
`storage_health`	Disk usage and I/O statistics analysis	(none)

Connect an MCP client

Claude Desktop

Add the following entry to your claude_desktop_config.json (~/Library/Application Support/Claude/claude_desktop_config.json on macOS, %APPDATA%\Claude\claude_desktop_config.json on Windows):

{
  "mcpServers": {
    "glances": {
      "url": "http://localhost:61208/mcp/sse"
    }
  }
}

For a password-protected server, use the headers field:

{
  "mcpServers": {
    "glances": {
      "url": "http://localhost:61208/mcp/sse",
      "headers": {
        "Authorization": "Basic <base64(user:password)>"
      }
    }
  }
}

Python MCP client (programmatic access)

import asyncio
from mcp.client.sse import sse_client
from mcp import ClientSession

async def main():
    async with sse_client("http://localhost:61208/mcp/sse") as (read, write):
        async with ClientSession(read, write) as session:
            await session.initialize()

            # List available resources
            resources = await session.list_resources()
            print([str(r.uri) for r in resources.resources])

            # Read CPU stats
            from pydantic import AnyUrl
            result = await session.read_resource(AnyUrl("glances://stats/cpu"))
            print(result.contents[0].text)

            # Run a health-summary prompt
            prompt = await session.get_prompt("system_health_summary")
            print(prompt.messages[0].content.text[:200])

asyncio.run(main())