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BSD-3-Clause

CLIO Plot MCP Server

io.github.iowarp/plot-mcp

Generate plots from CSV data with AI assistance for scientific visualization

What is the CLIO Plot MCP server?

The CLIO Plot MCP server generates plots from CSV data files. It provides tools for creating visualizations like line plots with proper axis labels and data exploration, enabling AI agents to produce publication-ready charts from tabular scientific data.

Plot MCP lets you create data visualizations programmatically through natural language. Ask an AI agent to plot temperature trends, compare datasets, or explore relationships in your CSV files—the server handles chart generation, formatting, and export. Part of CLIO Kit's 22 scientific computing servers.

How to install CLIO Plot

Copy-paste configuration for popular MCP clients.

transport: stdio
Config generated by PluginBench — verify against the source before use.
~/Library/Application Support/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "plot-mcp": {
      "command": "uvx",
      "args": [
        "clio-kit"
      ]
    }
  }
}

Tools & capabilities

Tools this server exposes to the agent.

  • line_plot — Create line plots showing trends over time or relationships between variables
  • data_info — Inspect and summarize CSV data structure, columns, and statistics

Use cases

  • Generate line plots showing temperature or measurement trends over time from CSV files
  • Create multi-series plots comparing different datasets or experimental conditions
  • Explore data distributions and relationships visually before statistical analysis
  • Export publication-ready charts with proper axis labels and legends
  • Automate repetitive plotting tasks in data analysis workflows

CLIO Plot MCP server FAQ

What is the CLIO Plot MCP server?

Plot MCP is a visualization tool that generates charts from CSV data. It supports line plots and data exploration, letting AI agents create plots through natural language requests.

Is it free?

Yes. CLIO Kit, including Plot MCP, is open-source under the BSD-3-Clause license.

How do I install it in Cursor?

Add to ~/.cursor/mcp.json: {"plot-mcp": {"command": "clio-kit", "args": ["mcp-server", "plot"]}}. First install CLIO Kit via 'uv tool install clio-kit'.

How do I install it in Claude?

Run: claude mcp add plot-mcp -- clio-kit mcp-server plot

Does it require authentication?

No. Plot MCP works locally on your CSV files without requiring API keys or external authentication.

What data formats does it support?

Plot MCP reads CSV files. It's part of CLIO Kit, which also includes servers for HDF5, Parquet, and other scientific data formats.

README (reference)

Source of truth, from the repository.

CLIO Kit

<!-- mcp-name: io.github.iowarp/adios-mcp --> <!-- mcp-name: io.github.iowarp/arxiv-mcp --> <!-- mcp-name: io.github.iowarp/chronolog-mcp --> <!-- mcp-name: io.github.iowarp/compression-mcp --> <!-- mcp-name: io.github.iowarp/darshan-mcp --> <!-- mcp-name: io.github.iowarp/geo-mcp --> <!-- mcp-name: io.github.iowarp/geojson-mcp --> <!-- mcp-name: io.github.iowarp/hdf5-mcp --> <!-- mcp-name: io.github.iowarp/jarvis-mcp --> <!-- mcp-name: io.github.iowarp/lmod-mcp --> <!-- mcp-name: io.github.iowarp/ndp-mcp --> <!-- mcp-name: io.github.iowarp/node-hardware-mcp --> <!-- mcp-name: io.github.iowarp/pandas-mcp --> <!-- mcp-name: io.github.iowarp/parallel-sort-mcp --> <!-- mcp-name: io.github.iowarp/paraview-mcp --> <!-- mcp-name: io.github.iowarp/parquet-mcp --> <!-- mcp-name: io.github.iowarp/plot-mcp --> <!-- mcp-name: io.github.iowarp/sac-mcp --> <!-- mcp-name: io.github.iowarp/scientific-catalog-mcp --> <!-- mcp-name: io.github.iowarp/seismic-mcp --> <!-- mcp-name: io.github.iowarp/slurm-mcp --> <!-- mcp-name: io.github.iowarp/spack-mcp --> <!-- mcp-name: io.github.iowarp/terrain-mcp --> <!-- mcp-name: io.github.iowarp/web-mcp -->

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. It ships 22 Model Context Protocol (MCP) servers for scientific computing and 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 <img src="https://grc.iit.edu/img/logo.png" alt="GRC Logo" width="18" height="18"> 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. 22 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, BSD-3-Clause licensed, and live-tested in real HPC environments.

🚀 Quick Installation

One Command for Any Server

# Install the released CLI into its own persistent tool environment
uv tool install 'clio-kit==2.4.3'
# If uv reports that its executable directory is not on PATH:
uv tool update-shell

# List all 23 available MCP servers
clio-kit mcp-servers

# Run any installed server
clio-kit mcp-server hdf5
clio-kit mcp-server pandas
clio-kit mcp-server slurm

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

# AI prompts also available
clio-kit prompts                    # List all prompts
clio-kit prompt code-coverage-prompt # Use a prompt

uv tool install keeps CLIO Kit in a persistent, isolated tool environment. Use uvx --from 'clio-kit==2.4.3' clio-kit ... only for a temporary, one-shot invocation.

Released clio-kit wheels execute each embedded MCP server from that server's shipped uv.lock. The launcher uses a source-and-lock-addressed environment under the user cache, installs only production dependencies, and refuses to resolve an embedded server whose lock is missing. The --branch launcher option is an explicit development path and is not an immutable release-artifact path.

The root wheel also ships machine-readable user contracts for the locked JARVIS, SLURM, Spack, and Scientific Catalog servers. These artifacts are generated from real stdio tools/list exchanges and include canonical SHA-256 digests for downstream federation gates:

clio-kit mcp-contracts
clio-kit mcp-contract clio-kit-jarvis-user-v3.5
clio-kit mcp-contract clio-kit-slurm-user-v3
clio-kit mcp-contract clio-kit-spack-user-v2.1
clio-kit mcp-contract clio-kit-scientific-catalog-user-v1.1
<details> <summary><b>Install in Cursor</b></summary>

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

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

See Cursor MCP docs for more info.

</details> <details> <summary><b>Install in Claude Code</b></summary>
# Add HDF5 MCP
claude mcp add hdf5-mcp -- clio-kit mcp-server hdf5

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

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

See Claude Code MCP docs for more info.

</details> <details> <summary><b>Install in VS Code</b></summary>

Add to your VS Code MCP config:

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

See VS Code MCP docs for more info.

</details> <details> <summary><b>Install in Claude Desktop</b></summary>

Edit claude_desktop_config.json:

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

See Claude Desktop MCP docs for more info.

</details>

Available Packages

The version below is each MCP server's agent-facing contract version, not the containing clio-kit wheel version. JARVIS 3.4 and SLURM 3.0 have contracts redesigned for agent use. Spack is at 2.1, while the other contracts retain their existing 2.x identities until a focused upgrade.

The Spack install contract makes concretization explicit: reuse=true passes spack install --reuse, while reuse=false passes spack install --fresh. Agents should discover first, install only when needed, then pass the exact spack_locate result to JARVIS for runtime loading. A find with no installed match is normal typed data (count=0, packages=[]); locate reports the distinct not_installed semantic, while real Spack failures remain errors.

<div align="center">
📦 Package📌 Ver🔧 System📋 Description⚡ Install Command
adios2.2.3Data I/ORead data using ADIOS2 engineclio-kit mcp-server adios
arxiv2.2.3ResearchFetch research papers from ArXivclio-kit mcp-server arxiv
chronolog2.0.1LoggingLog and retrieve data from ChronoLogclio-kit mcp-server chronolog
compression2.2.3UtilitiesFile compression with gzipclio-kit mcp-server compression
darshan2.2.3PerformanceI/O performance trace analysisclio-kit mcp-server darshan
geo2.2.3GeospatialRender GeoJSON vector layers with basemapsclio-kit mcp-server geo
geojson2.2.3GeospatialInspect, validate, and summarize GeoJSONclio-kit mcp-server geojson
hdf52.2.3Data I/OHPC-optimized scientific data with 27 tools, AI insights, caching, streamingclio-kit mcp-server hdf5
jarvis3.7.2WorkflowDurable pipeline, bounded package discovery, progress, artifact, and service-runtime managementclio-kit mcp-server jarvis
lmod2.2.3EnvironmentEnvironment module managementclio-kit mcp-server lmod
ndp2.2.3Data ProtocolSearch and discover datasets across CKAN instancesclio-kit mcp-server ndp
node-hardware2.2.3SystemSystem hardware informationclio-kit mcp-server node-hardware
pandas2.2.3Data AnalysisCSV data loading and filteringclio-kit mcp-server pandas
parallel-sort2.2.3ComputingLarge file sortingclio-kit mcp-server parallel-sort
paraview2.2.3VisualizationScientific 3D visualization and analysisclio-kit mcp-server paraview
parquet2.2.3Data I/ORead Parquet file columnsclio-kit mcp-server parquet
plot2.2.3VisualizationGenerate plots from CSV dataclio-kit mcp-server plot
sac2.2.3SeismologyAnalyze SAC waveforms and archivesclio-kit mcp-server sac
seismic2.2.3SeismologyAnalyze earthquake catalogs and sequencesclio-kit mcp-server seismic
scientific-catalog1.1.2DiscoveryOperator-owned scientific dataset discoveryclio-kit mcp-server scientific-catalog
slurm3.0.0HPCJob submission and managementclio-kit mcp-server slurm
spack2.1.0Package ManagementStructured package discovery, installation, and locationclio-kit mcp-server spack
terrain2.2.3GeospatialAnalyze DEMs and terrain point cloudsclio-kit mcp-server terrain
web2.1.0WebSynchronous search plus durable, queryable, cancellable URL, DOI, and document fetch tasksclio-kit mcp-server web
</div>

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
clio-kit search serve

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

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

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

API endpoints: /query, /jobs/index, /documents, /health, /metrics — full 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: clio-kit search query --namespace local_fs --q "pressure > 200 kPa"


🚨 Troubleshooting

<details> <summary><b>Server Not Found Error</b></summary>

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

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

# Common names: hdf5, pandas, slurm, arxiv (not hdf5-mcp, pandas-mcp)
</details> <details> <summary><b>Import Errors or Missing Dependencies</b></summary>

For development or local testing:

cd clio-kit-mcp-servers/hdf5
uv sync --all-extras --dev
uv run hdf5-mcp
</details> <details> <summary><b>uv or clio-kit Command Not Found</b></summary>

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

Then install CLIO Kit persistently and expose uv's tool directory:

uv tool install 'clio-kit==2.4.3'
uv tool update-shell
</details>

Team

Sponsored By

<img src="https://www.nsf.gov/themes/custom/nsf_theme/components/molecules/logo/logo-desktop.png" alt="NSF Logo" width="24" height="24"> 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


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