Performance Co-Pilot

Name: Performance Co-Pilot
Author: major

by majorversion 1.5.1i★ –iRuns on your computer

Registry nameio.github.major/pcp

Install

Overview

Description from the maker (original English)

Query system performance metrics via MCP - CPU, memory, disk I/O, network, processes

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: PCP_HOST, PCP_PORT, PCP_TARGET_HOST

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "pcp": {
      "command": "uvx",
      "args": [
        "pcp-mcp"
      ],
      "env": {
        "PCP_HOST": "your-value",
        "PCP_PORT": "your-value",
        "PCP_TARGET_HOST": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add pcp --env PCP_HOST=your-value --env PCP_PORT=your-value --env PCP_TARGET_HOST=your-value -- uvx pcp-mcp

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "pcp": {
      "command": "uvx",
      "args": [
        "pcp-mcp"
      ],
      "env": {
        "PCP_HOST": "your-value",
        "PCP_PORT": "your-value",
        "PCP_TARGET_HOST": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "pcp": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "pcp-mcp"
      ],
      "env": {
        "PCP_HOST": "your-value",
        "PCP_PORT": "your-value",
        "PCP_TARGET_HOST": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

PCP_HOST env
pmproxy host
PCP_PORT env
pmproxy port
PCP_TARGET_HOST env
Target pmcd host to monitor

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account major, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 7247 servers in this category
Published to the registry more recently than 7% of the other 7247 servers in this category
Basic checks equal to or better than 100% of the other 7247 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Performance Co-Pilot safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.5.1) reached the registry 7 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated today

Install

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 20 days ago

Install

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated last month

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

pcp-mcp

MCP server for Performance Co-Pilot (PCP) metrics.

Query system performance metrics via the Model Context Protocol - CPU, memory, disk I/O, network, processes, and more.

📖 [Full Documentation](https://major.github.io/pcp-mcp) | 🚀 [Getting Started](https://major.github.io/pcp-mcp/getting-started/)

🚀 Quick Start (No Install)

Run immediately with uvx — no installation required:

uvx pcp-mcp

Or install as a persistent global tool:

uvx tool install pcp-mcp

pcp-mcp

📦 Installation

pip install pcp-mcp

Or with uv:

uv add pcp-mcp

📋 Requirements

Python: 3.10+
PCP: Performance Co-Pilot with pmcd and pmproxy running

  # Fedora/RHEL/CentOS

sudo dnf install pcp sudo systemctl enable --now pmcd pmproxy

Ubuntu/Debian

sudo apt install pcp sudo systemctl enable --now pmcd pmproxy

⚙️ Configuration

Configure via environment variables:

Variable	Description	Default
`PCP_HOST`	pmproxy host	`localhost`
`PCP_PORT`	pmproxy port	`44322`
`PCP_TARGET_HOST`	Target pmcd host to monitor	`localhost`
`PCP_USE_TLS`	Use HTTPS for pmproxy	`false`
`PCP_TLS_VERIFY`	Verify TLS certificates	`true`
`PCP_TLS_CA_BUNDLE`	Path to custom CA bundle	(optional)
`PCP_TIMEOUT`	Request timeout (seconds)	`30`
`PCP_USERNAME`	HTTP basic auth user	(optional)
`PCP_PASSWORD`	HTTP basic auth password	(optional)
`PCP_ALLOWED_HOSTS`	Hostspecs allowed via host param	(optional)

🎯 Usage

Monitor localhost (default)

pcp-mcp

Monitor a remote host

PCP_TARGET_HOST=webserver1.example.com pcp-mcp

Or use the CLI flag:

pcp-mcp --target-host webserver1.example.com

Connect to remote pmproxy

PCP_HOST=metrics.example.com pcp-mcp

Use SSE transport

pcp-mcp --transport sse

🔌 MCP Client Configuration

Claude Desktop

Add to ~/.config/claude/claude_desktop_config.json:

"mcpServers": { "pcp": { "command": "uvx", "args": ["pcp-mcp"] } } }

For remote monitoring:

"mcpServers": { "pcp": { "command": "uvx", "args": ["pcp-mcp", "--target-host", "webserver1.example.com"] } } }

💡 Using uvx means you don't need pcp-mcp installed — it runs directly from PyPI.

🛠️ Available Tools

System Monitoring

`get_system_snapshot` - Point-in-time system overview (CPU, memory, disk, network, load)
`get_process_top` - Top processes by CPU, memory, or I/O usage
`query_metrics` - Fetch current values for specific PCP metrics
`search_metrics` - Discover available metrics by name pattern
`describe_metric` - Get detailed metadata about a metric

Example Queries

"What's the current CPU usage?"

→ Uses get_system_snapshot

"Show me the top 10 processes by memory usage" → Uses get_process_top(sort_by="memory", limit=10)

"What metrics are available for network traffic?" → Uses search_metrics(pattern="network")

"Get detailed info about kernel.all.load" → Uses describe_metric(name="kernel.all.load")

💡 Use Cases

Performance Troubleshooting

Ask Claude to:

"Analyze current system performance and identify bottlenecks"
"Why is my disk I/O so high?"
"Which processes are consuming the most CPU?"

System Monitoring

"Give me a health check of the production server"
"Compare CPU usage over the last minute"
"Monitor network traffic on eth0"

Capacity Planning

"What's the memory utilization trend?"
"Show me disk usage across all filesystems"
"Analyze process resource consumption patterns"

🏗️ Architecture

┌─────────┐         ┌─────────┐          ┌─────────┐         ┌─────────┐

│ LLM │ ◄─MCP─► │ pcp-mcp │ ◄─HTTP─► │ pmproxy │ ◄─────► │ pmcd │ └─────────┘ └─────────┘ └─────────┘ └─────────┘ (REST API) (metrics)

pcp-mcp: FastMCP server exposing PCP metrics via MCP tools
pmproxy: PCP's REST API server (runs on port 44322 by default)
pmcd: PCP metrics collector daemon
Remote monitoring: Set PCP_TARGET_HOST to query a different pmcd instance via pmproxy

🔧 Development

# Install dependencies

uv sync --dev

Run all checks

make check

Individual commands

make lint # ruff check make format # ruff format make typecheck # ty check make test # pytest with coverage

📖 Documentation

Full documentation at https://major.github.io/pcp-mcp

📄 License

MIT

Performance Co-Pilot

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

pcp-mcp

🚀 Quick Start (No Install)

📦 Installation

📋 Requirements

Ubuntu/Debian

⚙️ Configuration

🎯 Usage

Monitor localhost (default)

Monitor a remote host

Connect to remote pmproxy

Use SSE transport

🔌 MCP Client Configuration

Claude Desktop

🛠️ Available Tools

System Monitoring

Example Queries

💡 Use Cases

Performance Troubleshooting

System Monitoring

Capacity Planning

🏗️ Architecture

🔧 Development

Run all checks

Individual commands

📖 Documentation

📄 License