.. _api_mcp:

MCP (Model Context Protocol) server

Glances can expose its system monitoring data through a Model Context Protocol <https://modelcontextprotocol.io>_ (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:

.. code-block:: bash

pip install 'glances[mcp]'

Start the MCP server

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

.. code-block:: bash

glances -w --enable-mcp

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

The SSE endpoint used by MCP clients is:

.. code-block:: text

http://localhost:61208/mcp/sse

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

.. code-block:: bash

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 :ref:api_restful 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.

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):

.. code-block:: json

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

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

.. code-block:: json

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

Python MCP client (programmatic access)

.. code-block:: python

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())

.. seealso::

:ref:api_restful — RESTful/JSON API documentation

:ref:cmds — full command-line reference