About Performance Co-Pilot
Performance Co-Pilot is an MCP server for querying system performance metrics from PCP. It serves operators monitoring local or remote hosts, with tools for snapshots, process rankings, metric queries, discovery, and descriptions.
Key features
- 01Get CPU, memory, disk, and network snapshots
- 02Rank processes by CPU, memory, or I/O
- 03Query current PCP metric values
- 04Search available metrics by pattern
- 05Retrieve metric metadata
Use cases
- 01Investigate system performance bottlenecks
- 02Check production server health
- 03Find processes with high resource use
- 04Review metrics for capacity planning
MCP support
Exposes PCP metrics to MCP clients through tools for system snapshots, process rankings, metric queries, metric search, and metric descriptions. It connects to pmproxy and can target a remote pmcd host.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
uvx pcp-mcpPCP_HOSTpmproxy hostPCP_PORTpmproxy portPCP_TARGET_HOSTTarget pmcd host to monitorLocal servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEpcp-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 | 🚀 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
pmcdandpmproxyrunning# 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
uvxmeans 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 usagequery_metrics- Fetch current values for specific PCP metricssearch_metrics- Discover available metrics by name patterndescribe_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_HOSTto 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
Comments
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From what the protocol is to installing your first server, these answers cover most first questions.
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