CLIO Darshan
Let your coding agent interpret Darshan I/O trace files when you are chasing slow disk or filesystem bottlenecks on jobs you run locally or in the cloud.
Overview
io.github.iowarp/darshan-mcp is a MCP server for the Operate phase that lets AI agents analyze Darshan I/O profiler trace files through stdio MCP tools.
What is this MCP server?
- Exposes Darshan I/O profiler workflows to MCP clients over stdio transport
- Ships as part of the CLIO Kit (clio-kit on PyPI, version 2.2.3)
- Focused on parsing and analyzing I/O trace files rather than generic log tailing
- Fits scientific, ML, and data-heavy pipelines where filesystem patterns dominate latency
- Open-source source repo at iowarp/clio-kit on GitHub
- MCP server schema version 2.2.3
- Single PyPI package identifier clio-kit with stdio transport
- Published under io.github.iowarp/darshan-mcp in the MCP registry
Community signal: 25 GitHub stars.
What problem does it solve?
You have Darshan trace files from a slow job but explaining read/write hotspots to your agent means manual CLI steps and fragmented copy-paste.
Who is it for?
Indie builders running Python or HPC-style workloads who already generate Darshan traces and want MCP-native profiling help inside Claude Code or Cursor.
Skip if: Teams that only need simple web app APM with no trace files, or anyone unwilling to install Python-side CLIO Kit dependencies.
What do I get? / Deliverables
After you add the server, your agent can query and interpret I/O traces in context so you can adjust code, mounts, or job parameters with evidence instead of guesswork.
- Agent-accessible analysis of Darshan I/O trace content
- Actionable summaries of read/write patterns for tuning discussions
- In-chat profiling workflow without leaving the coding agent
Recommended MCP Servers
Journey fit
I/O trace analysis is something you reach for after the system is running and performance or storage behavior looks wrong, not while you are still sketching product ideas. Monitoring is the canonical shelf because profiler output answers what the workload actually read and wrote, which is how you debug production-like runs and recurring batch jobs.
How it compares
Specialized I/O profiler MCP bridge, not a general application error-tracking or log aggregation skill.
Common Questions / FAQ
Who is io.github.iowarp/darshan-mcp for?
It is for developers and solo builders who use Darshan to profile storage I/O and want their MCP agent to read and reason about those traces during performance investigations.
When should I use io.github.iowarp/darshan-mcp?
Use it when a batch job, training run, or data pipeline is I/O bound and you need the agent to help interpret Darshan output while you iterate on code or infrastructure.
How do I add io.github.iowarp/darshan-mcp to my agent?
Install the clio-kit package from PyPI (2.2.3), configure stdio MCP for io.github.iowarp/darshan-mcp in Claude Code or Cursor per your client’s MCP settings, and point tools at your trace file paths.