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CLIO Chronolog MCP

  • 26 repo stars
  • Updated August 7, 2026
  • iowarp/clio-kit

io.github.iowarp/chronolog-mcp is a MCP server that connects agents to ChronoLog for chronological logging over Model Context Protocol.

About

io.github.iowarp/chronolog-mcp (CLIO Chronolog) provides a Model Context Protocol server for ChronoLog so coding agents can work with chronological logging data during operations and incident review. developers running experiments, distributed jobs, or research infra can ask the agent to reason over event timelines instead of only grepping flat files. The package ships at version 2.0.1 through clio-kit on PyPI with stdio transport, alongside sibling CLIO MCP tools from iowarp. Public README detail is thin, so treat capabilities as ChronoLog-oriented log access rather than a full hosted observability platform. Best when you already use or evaluate ChronoLog in your stack and want MCP glue for Claude Code or Cursor.

  • ChronoLog MCP bridge from iowarp CLIO Kit (version 2.0.1)
  • Stdio Model Context Protocol transport via clio-kit on PyPI
  • Part of the same clio-kit repo as ADIOS, Arxiv, and Compression servers
  • Agent-oriented access to chronological log workflows without custom dashboards first
  • Useful when correlating incidents with timed events in technical pipelines

CLIO Chronolog MCP by the numbers

  • Data as of Aug 10, 2026 (Skillselion catalog sync)
terminal
claude mcp add clio-kit -- uvx clio-kit

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repo stars26
Packageclio-kit
TransportSTDIO
AuthNone
Last updatedAugust 7, 2026
Repositoryiowarp/clio-kit

What it does

Expose ChronoLog time-series or event log data to your agent when debugging production or lab runs.

Who is it for?

Technical solos operating ChronoLog or CLIO-adjacent pipelines who want MCP-based log investigation.

Skip if: Skip if you need turnkey SaaS APM with zero ChronoLog or HPC logging context.

What you get

Agents can query ChronoLog-backed timelines so you narrow outages and regressions faster during operate workflows.

  • Agent tools for querying ChronoLog-oriented chronological data
  • Faster correlation narratives during post-incident review
  • MCP integration alongside other CLIO Kit scientific servers

By the numbers

  • Server version 2.0.1
  • Distributed as clio-kit on PyPI with stdio transport
  • Source repository github.com/iowarp/clio-kit
README.md

CLIO Kit

License: BSD-3-Clause PyPI version Python FastMCP CI Coverage

MCP Servers Ruff Type Checked Package Manager Security Audit

CLIO Kit - Part of the IoWarp platform's tooling layer for AI agents. A comprehensive collection of tools, skills, plugins, and extensions. Currently featuring 15+ Model Context Protocol (MCP) servers for scientific computing, with plans to expand to additional agent capabilities. Enables AI agents to interact with HPC resources, scientific data formats, and research datasets.

Website | IOWarp

Chat with us on Zulip or join us

Developed by GRC Logo Gnosis Research Center


❌ Without CLIO Kit

Working with scientific data and HPC resources requires manual scripting and tool-specific knowledge:

  • ❌ Write custom scripts for every HDF5/Parquet file exploration
  • ❌ Manually craft Slurm job submission scripts
  • ❌ Switch between multiple tools for data analysis
  • ❌ No AI assistance for scientific workflows
  • ❌ Repetitive coding for common research tasks

✅ With CLIO Kit

AI agents handle scientific computing tasks through natural language:

  • "Analyze the temperature dataset in this HDF5 file" - HDF5 MCP does it
  • "Submit this simulation to Slurm with 32 cores" - Slurm MCP handles it
  • "Find papers on neural networks from ArXiv" - ArXiv MCP searches
  • "Plot the results from this CSV file" - Plot MCP visualizes
  • "Optimize memory usage for this pandas DataFrame" - Pandas MCP optimizes
  • "Find all documents where pressure exceeds 200 kPa" - Agentic Search retrieves

One unified interface. 16 MCP servers. Hybrid search engine. 150+ specialized tools. Built for research.

CLIO Kit is part of the IoWarp platform's comprehensive tooling ecosystem for AI agents. It brings AI assistance to your scientific computing workflow—whether you're analyzing terabytes of HDF5 data, managing Slurm jobs across clusters, or exploring research papers. Built by researchers, for researchers, at Illinois Institute of Technology with NSF support.

Part of IoWarp Platform: CLIO Kit is the tooling layer of the IoWarp platform, providing skills, plugins, and extensions for AI agents working in scientific computing environments.

One simple command. Production-ready, fully typed, MIT licensed, and beta-tested in real HPC environments.

🚀 Quick Installation

One Command for Any Server

# List all 16 available MCP servers
uvx clio-kit mcp-servers

# Run any server instantly
uvx clio-kit mcp-server hdf5
uvx clio-kit mcp-server pandas
uvx clio-kit mcp-server slurm

# Agentic search — hybrid retrieval for scientific corpora
uvx clio-kit search serve               # Start search API server
uvx clio-kit search query --namespace local_fs --q "pressure > 200 kPa"

# AI prompts also available
uvx clio-kit prompts                    # List all prompts
uvx clio-kit prompt code-coverage-prompt # Use a prompt
Install in Cursor

Add to your Cursor ~/.cursor/mcp.json:

{
  "mcpServers": {
    "hdf5-mcp": {
      "command": "uvx",
      "args": ["clio-kit", "mcp-server", "hdf5"]
    },
    "pandas-mcp": {
      "command": "uvx",
      "args": ["clio-kit", "mcp-server", "pandas"]
    },
    "slurm-mcp": {
      "command": "uvx",
      "args": ["clio-kit", "mcp-server", "slurm"]
    }
  }
}

See Cursor MCP docs for more info.

Install in Claude Code
# Add HDF5 MCP
claude mcp add hdf5-mcp -- uvx clio-kit mcp-server hdf5

# Add Pandas MCP
claude mcp add pandas-mcp -- uvx clio-kit mcp-server pandas

# Add Slurm MCP
claude mcp add slurm-mcp -- uvx clio-kit mcp-server slurm

See Claude Code MCP docs for more info.

Install in VS Code

Add to your VS Code MCP config:

"mcp": {
  "servers": {
    "hdf5-mcp": {
      "type": "stdio",
      "command": "uvx",
      "args": ["clio-kit", "mcp-server", "hdf5"]
    },
    "pandas-mcp": {
      "type": "stdio",
      "command": "uvx",
      "args": ["clio-kit", "mcp-server", "pandas"]
    }
  }
}

See VS Code MCP docs for more info.

Install in Claude Desktop

Edit claude_desktop_config.json:

{
  "mcpServers": {
    "hdf5-mcp": {
      "command": "uvx",
      "args": ["clio-kit", "mcp-server", "hdf5"]
    },
    "arxiv-mcp": {
      "command": "uvx",
      "args": ["clio-kit", "mcp-server", "arxiv"]
    }
  }
}

See Claude Desktop MCP docs for more info.

Available Packages

📦 Package 📌 Ver 🔧 System 📋 Description Install Command
adios 2.0.1 Data I/O Read data using ADIOS2 engine uvx clio-kit mcp-server adios
arxiv 2.0.1 Research Fetch research papers from ArXiv uvx clio-kit mcp-server arxiv
chronolog 2.0.1 Logging Log and retrieve data from ChronoLog uvx clio-kit mcp-server chronolog
compression 2.0.1 Utilities File compression with gzip uvx clio-kit mcp-server compression
darshan 2.0.1 Performance I/O performance trace analysis uvx clio-kit mcp-server darshan
hdf5 2.0.1 Data I/O HPC-optimized scientific data with 27 tools, AI insights, caching, streaming uvx clio-kit mcp-server hdf5
jarvis 2.0.1 Workflow Data pipeline lifecycle management uvx clio-kit mcp-server jarvis
lmod 2.0.1 Environment Environment module management uvx clio-kit mcp-server lmod
ndp 2.0.1 Data Protocol Search and discover datasets across CKAN instances uvx clio-kit mcp-server ndp
node-hardware 2.0.1 System System hardware information uvx clio-kit mcp-server node-hardware
pandas 2.0.1 Data Analysis CSV data loading and filtering uvx clio-kit mcp-server pandas
parallel-sort 2.0.1 Computing Large file sorting uvx clio-kit mcp-server parallel-sort
paraview 2.0.1 Visualization Scientific 3D visualization and analysis uvx clio-kit mcp-server paraview
parquet 2.0.1 Data I/O Read Parquet file columns uvx clio-kit mcp-server parquet
plot 2.0.1 Visualization Generate plots from CSV data uvx clio-kit mcp-server plot
slurm 2.0.1 HPC Job submission and management uvx clio-kit mcp-server slurm

Agentic Search

Hybrid retrieval engine for scientific corpora — combines lexical (BM25), vector, graph, and scientific search (numeric range, unit matching, formula targeting) over namespaced document collections. DuckDB storage, FastAPI, async job queue, OpenTelemetry tracing, Prometheus metrics.

# Start the search API server
uvx clio-kit search serve

# Index documents from a namespace
uvx clio-kit search index --namespace local_fs

# Query with scientific operators
uvx clio-kit search query --namespace local_fs --q "pressure between 190 and 360 kPa"

# List indexed documents
uvx clio-kit search list --namespace local_fs

API endpoints: /query, /jobs/index, /documents, /health, /metricsfull docs


📖 Usage Examples

HDF5: Scientific Data Analysis

"What datasets are in climate_simulation.h5? Show me the temperature field structure and read the first 100 timesteps."

Tools used: open_file, analyze_dataset_structure, read_partial_dataset, list_attributes

Slurm: HPC Job Management

"Submit simulation.py to Slurm with 32 cores, 64GB memory, 24-hour runtime. Monitor progress and retrieve output when complete."

Tools used: submit_slurm_job, check_job_status, get_job_output

ArXiv: Research Discovery

"Find the latest papers on diffusion models from ArXiv, get details on the top 3, and export citations to BibTeX."

Tools used: search_arxiv, get_paper_details, export_to_bibtex, download_paper_pdf

Pandas: Data Processing

"Load sales_data.csv, clean missing values, compute statistics by region, and save as Parquet with compression."

Tools used: load_data, handle_missing_data, groupby_operations, save_data

Plot: Data Visualization

"Create a line plot showing temperature trends over time from weather.csv with proper axis labels."

Tools used: line_plot, data_info

Agentic Search: Scientific Retrieval

"Find all chunks mentioning pressure above 200 kPa in the local_fs namespace."

CLI: uvx clio-kit search query --namespace local_fs --q "pressure > 200 kPa"


🚨 Troubleshooting

Server Not Found Error

If uvx clio-kit mcp-server <server-name> fails:

# Verify server name is correct
uvx clio-kit mcp-servers

# Common names: hdf5, pandas, slurm, arxiv (not hdf5-mcp, pandas-mcp)
Import Errors or Missing Dependencies

For development or local testing:

cd clio-kit-mcp-servers/hdf5
uv sync --all-extras --dev
uv run hdf5-mcp
uvx Command Not Found

Install uv package manager:

# Linux/macOS
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

# Or via pip
pip install uv

Team

Sponsored By

NSF Logo NSF (National Science Foundation) - Supporting scientific computing research and AI integration initiatives

we welcome more sponsorships. please contact the Principal Investigator

Ways to Contribute

  • Submit Issues: Report bugs or request features via GitHub Issues
  • Develop New MCPs: Add servers for your research tools (CONTRIBUTING.md)
  • Improve Documentation: Help make guides clearer
  • Share Use Cases: Tell us how you're using CLIO Kit in your research

Full Guide: CONTRIBUTING.md

Community & Support


Recommended MCP Servers

How it compares

ChronoLog-focused logging MCP, not a hosted Datadog-style observability product.

FAQ

Who is io.github.iowarp/chronolog-mcp for?

Operators and developers using ChronoLog who want agent-assisted review of chronological events and logs.

When should I use io.github.iowarp/chronolog-mcp?

Use it in operate and monitoring when you need MCP tools to inspect timed logs or chronology data during debugging.

How do I add io.github.iowarp/chronolog-mcp to my agent?

Configure the CLIO Chronolog MCP stdio server from clio-kit (2.0.1) in your MCP client per the clio-kit documentation.

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