UXarray MCP Server

An MCP server that exposes UXarray tools to AI clients such as Claude. It supports:

• local execution for normal mesh analysis
• optional remote execution on HPC systems through Globus Compute
• diagnostics and provenance for scientific workflows

How it runs

The server runs as a subprocess of the MCP client (Claude Code, Claude Desktop, or any FastMCP transport) and dispatches tool calls either locally or to a configured Globus Compute endpoint on an HPC cluster. Same MCP tool, same schema — use_remote: bool on every tool decides which path runs.

Local mode — analysis on your machine, files on your disk:

 ┌────────────────┐         ┌─────────────────────────────┐
 │  Claude Code   │  stdio  │  uv run python -m           │      reads
 │  / Desktop     │ ◀─────▶ │  uxarray_mcp                │ ───▶  local
 │                │  pipe   │  (subprocess on your box)   │       mesh files
 └────────────────┘         └─────────────────────────────┘

HPC mode — analysis on facility hardware, files stay on facility storage:

 ┌────────────────┐  stdio  ┌─────────────────────────┐  Globus  ┌─────────────────────────┐
 │  Claude Code   │ ◀─────▶ │  uxarray_mcp on laptop  │ ◀──────▶ │  Worker on HPC endpoint │      reads
 │  / Desktop     │  pipe   │  (dispatches when       │  Compute │  (uxarray reads file    │ ───▶ facility
 │                │         │   use_remote=True)      │  RPC     │   from facility GPFS)   │      mesh files
 └────────────────┘         └─────────────────────────┘          └─────────────────────────┘      (never copied)

The dispatcher falls back to local execution if a remote call is requested but the endpoint is missing or unhealthy.

Install

# Stable user path (after the package is published)
uv tool install uxarray-mcp
uxarray-mcp setup
uxarray-mcp install-claude --print-only   # prints the Claude Desktop block

# Stable user path with HPC support
uv tool install "uxarray-mcp[hpc]"
uxarray-mcp setup
uxarray-mcp endpoints add improv <your-endpoint-uuid> --set-default
uxarray-mcp doctor --endpoint improv --timeout-seconds 180

# Developer / contributor path, and best path when using repo scripts/docs
git clone https://github.com/UXARRAY/uxarray-mcp-server.git
cd uxarray-mcp-server
uv sync                 # core local install
uv sync --extra hpc     # add Globus Compute deps

# User install directly from GitHub before a PyPI release exists
uv tool install "git+https://github.com/UXARRAY/uxarray-mcp-server.git"
uxarray-mcp setup
uxarray-mcp endpoints add improv <your-endpoint-uuid>
uxarray-mcp install-claude --print-only   # prints the Claude Desktop block

The uxarray-mcp CLI exposes:

subcommand	what it does
serve	run the MCP server on stdio (Claude / FastMCP transport)
setup	write a starter config to ~/.config/uxarray-mcp/
endpoints add	register a named Globus Compute endpoint
endpoints list	show configured endpoints + discovery path
doctor	validate auth, endpoint health, optional remote probes
install-claude	print or merge the Claude Desktop mcpServers block

Config is discovered in this order: $UXARRAY_MCP_CONFIG → ~/.config/uxarray-mcp/config.yaml → ./config.yaml (repo root).

Most Users Should Read These in Order

1. GETTING_STARTED.md for the short setup path
2. docs/getting-started.md for the full walkthrough
3. docs/globus-compute.md if you are new to Globus Compute
4. docs/hpc.md for generic cluster bring-up
5. docs/improv.md if you are on Argonne Improv
6. docs/ucar.md if you are on NCAR Casper
7. docs/chrysalis.md if you are on Argonne Chrysalis
8. docs/workflows.md for sequential remote workflows

Main Tools

Analysis:

• inspect_mesh — topology, format detection, face/node/edge counts
• inspect_variable — variable metadata, location, and statistics
• calculate_area — face area statistics
• calculate_zonal_mean — latitude-band averaging (conservative or standard)
• validate_dataset — NaN, Inf, and fill value checks
• run_scientific_agent — autonomous Analyze → Plan → Execute → Verify pipeline
• subset_bbox / subset_polygon / extract_cross_section — spatial queries and regional reductions
• calculate_gradient, calculate_curl, calculate_divergence, calculate_azimuthal_mean — vector calculus and radial summaries
• compare_fields, calculate_bias, calculate_rmse, calculate_pattern_correlation — same-grid comparison metrics
• remap_variable / regrid_dataset — UXarray-backed remapping to a target grid
• calculate_temporal_mean, calculate_anomaly, calculate_ensemble_mean, calculate_ensemble_spread — temporal and ensemble summaries
• export_to_netcdf, export_to_csv, write_result — persist derived results to downstream formats

Stateful workflows:

• create_session, register_dataset, get_session_state, reset_session_state
• run_workflow, resume_workflow, get_workflow_status
• get_result_handle, get_operation_status, list_operations

Visualization (returns inline PNG):

• plot_mesh — mesh wireframe
• plot_mesh_geo — geographic mesh plot with boundary-aware rendering
• plot_variable — face-centered variable as filled polygon map; supports cmap, vmin, vmax, title
• plot_zonal_mean — latitude vs. value line chart; supports line_color, title

HPC diagnostics:

• get_execution_mode / set_execution_mode
• endpoint_status
• validate_hpc_setup
• probe_path_access

All inspection, computation, and plotting tools accept use_remote: bool and endpoint: str | None. When use_remote=True the dispatcher submits to the configured (or named) Globus Compute endpoint and falls back to local execution if the endpoint is missing or unhealthy. There are no separate *_hpc tool names on the MCP surface — the same tool runs locally or remotely based on the flag.

Full parameter and return details live in docs/tools.md.

Helper Scripts

• scripts/hpc_doctor.py First-pass CLI doctor for local auth, endpoint status, remote no-op execution, and optional real-path probing.
• scripts/improv_endpoint.sh Writes Improv endpoint templates for single-host validation or PBS debug.
• scripts/agentic_hpc_loop.py Example submit/poll/branch workflow using Globus Compute futures directly.

HPC in One Paragraph

Remote execution has three separate layers:

1. the local machine running this repository
2. the endpoint running on the HPC machine
3. the remote worker environment that must also have uxarray, xarray, netCDF4, and h5netcdf

Most confusing failures happen because only one or two of those layers are set up. Start with docs/globus-compute.md and use validate_hpc_setup() before real remote jobs.

Configuration

Use the CLI for the common case:

uxarray-mcp setup
uxarray-mcp endpoints add improv <your-endpoint-uuid> --path-prefix /lus/ --set-default

This writes ~/.config/uxarray-mcp/config.yaml with the canonical multi-endpoint schema. For dev clones, ./config.yaml at the repo root still works (and is gitignored). The full schema:

hpc:
  default_endpoint: "ucar"
  endpoints:
    ucar:
      endpoint_id: "your-ucar-endpoint-uuid"
      path_prefixes: ["/glade/"]
    improv:
      endpoint_id: "your-improv-endpoint-uuid"
      path_prefixes: ["/gpfs/fs1/", "/home/jain/"]
  execution_mode: "auto"
  timeout_seconds: 300

Remote tools accept endpoint="ucar" or endpoint="improv"; when omitted, the server routes by path prefix before falling back to default_endpoint.

Development Checks

uv run pre-commit run --all-files
uv run pytest tests/ --ignore=tests/test_remote_agent.py
uv sync --extra docs --dev
uv run sphinx-build -b html docs docs/_build/html

Publishing

Releases follow the UXarray pattern: publish a GitHub Release from a version tag such as v0.1.0; the release workflow builds and publishes to PyPI with trusted publishing. Conda packages are handled through a separate conda-forge feedstock; conda/recipe/meta.yaml is a seed recipe for uxarray-mcp-feedstock.

uv build
uv tool install dist/uxarray_mcp-*.whl --force
uxarray-mcp --help

See docs/release.md for the full PyPI and Conda workflow.

Documentation Index

• GETTING_STARTED.md
• docs/getting-started.md
• docs/globus-compute.md
• docs/hpc.md
• docs/improv.md
• docs/ucar.md
• docs/chrysalis.md
• docs/tools.md
• docs/workflows.md
• docs/scientific-agent.md