mcp-local-telemetry
A lightweight MCP server that gives MCP-aware AI agents read-only access to local system telemetry (CPU, RAM, disk, top processes) over stdio JSON-RPC.
get_system_metrics— takes no arguments; returns current CPU %, RAM %, disk %, load average, and boot time.get_top_processes— returns the N processes with the highest CPU usage; optionallimit(integer, 1–50, default 5).Both tools are read-only, need no configuration or auth, and reject extra arguments (
additionalProperties: false).Enables agents to answer questions like "is it safe to run this build?" or "what's eating CPU?" — useful for pre-flight checks before heavy builds, containers, or model inference.
Protocol: JSON-RPC 2.0, newline-delimited over stdio; protocol version 2024-11-05; tools capability only; stdout carries only JSON-RPC frames (logs go to stderr).
Plugs into any MCP client (Claude Code, Cursor, Claude Desktop via
mcp.json, ChatGPT Desktop).
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@mcp-local-telemetry is it safe to run a build right now? check CPU and memory"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
mcp-local-telemetry
A lightweight Model Context Protocol server that exposes local system telemetry (CPU, RAM, Disk, top processes) to MCP-aware AI agents — Claude Code, Cursor, ChatGPT Desktop, and any other MCP client.
Written as a single-file, dependency-light Python script. Ships in under 200 lines and speaks JSON-RPC 2.0 over stdio.
Why
AI coding agents are increasingly asked to reason about the machine they are running on — "is it safe to run this build?", "what's eating CPU right now?". This server gives them a structured, protocol-standard way to ask.
Related MCP server: open-monitor
Tools
Tool | Description |
| CPU %, memory %, disk %, load average, boot time. |
| Top-N processes by CPU usage. |
Or install from source:
git clone https://github.com/Jamie643/mcp-local-telemetry.git cd mcp-local-telemetry python -m venv .venv && source .venv/bin/activate # Windows: .venv\Scripts\activate pip install -e .
Claude Code
claude mcp add local-telemetry -- mcp-local-telemetry
Cursor / Claude Desktop (mcp.json)
{ "mcpServers": { "local-telemetry": { "command": "mcp-local-telemetry" } } }
Manual smoke test
printf '%s\n'
'{"jsonrpc":"2.0","id":1,"method":"initialize","params":{}}'
'{"jsonrpc":"2.0","method":"notifications/initialized"}'
'{"jsonrpc":"2.0","id":2,"method":"tools/list"}'
'{"jsonrpc":"2.0","id":3,"method":"tools/call","params":{"name":"get_system_metrics","arguments":{}}}'
| mcp-local-telemetry
Development
pip install -e ".[dev]" ruff check . mypy mcp_server.py pytest
Protocol notes
Transport: JSON-RPC 2.0 over stdio, newline-delimited.
Protocol version: 2024-11-05.
Capabilities: tools only.
All logs go to stderr; stdout carries only JSON-RPC frames.
License
MIT — see LICENSE.
Commit it
git add README.md
git commit -m "Rewrite README: add badges, remove unrelated stack section, use PyPI install"
git push
## Install
```bash
pip install mcp-local-telemetryAvailable Tools
2 toolsget_system_metricsGet System MetricsA
Returns current CPU, RAM, and Disk utilisation for the local machine. Useful for pre-flight checks before launching heavy builds, containers, or model inference.
Inputs: no named parameters.
get_top_processesGet Top ProcessesC
Returns the N processes with the highest CPU usage.
Inputs: limit.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 2 tools
get_system_metrics returns aggregate machine-level stats while get_top_processes returns per-process ranking, so their purposes are clearly distinct. An agent can trivially choose between them with no overlap.
Both tools follow a consistent get_<noun> snake_case verb_noun pattern. Naming is predictable and idiomatic throughout.
Two tools is on the thin side for a telemetry server; while each earns its place, the surface feels minimal and could reasonably include a couple more monitoring dimensions without bloat.
Core CPU/RAM/Disk and top-CPU-process coverage exists, but network I/O, memory-heavy process ranking, disk I/O, and any historical/sampling data are missing. Agents can perform basic pre-flight checks but hit dead ends for richer diagnostics.
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