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Nc MCP

by · github.com/HanchaoLi/nc-mcp

A MCP Server for NetCDF format file

Unclaimed MIT last commit a year ago devtools
InstallView source
49Fair

Scored 2 days ago · breakdown

About Nc MCP

Nc MCP is an MCP server published by HanchaoLi in the Developer Tools category: a MCP Server for NetCDF format file. It has been installed 0 times through Conduid.

The repository has 1 stars and 0 forks, with the last commit a year ago. Six months or more without a commit doesn't mean the server is broken, but check the open issues (0) before depending on it in production.

Install

Install
npx nc-mcp

This server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.

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Security checks

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  • ·Tests presentNot checked yet.
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  • !Scoped permissionsDoesn't declare a permission scope. Assume it can do anything its process can.

Releases

0.1.3Release 0.1.3 · 24 Aug 2025Update README.md publish package to PyPI using github actions.

README

NetCDF Explorer MCP Server

A simple and powerful MCP (Message Communication Protocol) server for exploring, analyzing, and interacting with NetCDF format files. Designed to simplify NetCDF data management with intuitive APIs and efficient data handling.

Overview

NetCDF (Network Common Data Form) is a widely used format for storing scientific data. This MCP server provides a streamlined interface to work with NetCDF files, offering essential functionalities to list files, inspect structures, extract data, and analyze variables—all while handling large datasets efficiently through smart sampling.

Features

  • 📁 File Discovery: List all NetCDF files (.nc, .cdf, .netcdf, .nc4) in a specified directory.
  • 🔍 Structure Inspection: Retrieve detailed metadata of NetCDF files, including dimensions, variables, attributes, and global properties.
  • 📊 Data Extraction: Read variable data with support for flexible slicing (e.g., lat: "0:10:2" to extract subsets).
  • 🧠 Smart Sampling: Automatically sample large datasets to prevent memory overflow, with configurable element limits.
  • ⏰ Time Series Analysis: Extract temporal data from spatial variables, with optional custom location coordinates.
  • 🔎 Search Functionality: Find variables and attributes by keyword, simplifying navigation in complex files.
  • 📋 Variable Summaries: Generate human-readable summaries of variables, including dimensions, data types, and attributes.

Installation

Prerequisites

  • Python ≥ 3.13

Install via pip

pip install nc-mcp

Install from Source

  1. Clone the repository:

    git clone https://github.com/HanchaoLi/nc-mcp.git
cd nc-mcp

  2. Install dependencies (using uv for faster dependency management):

    # Install uv (if not already installed)
curl -LsSf https://astral.sh/uv/install.sh | sh

# Sync dependencies
uv sync --all-groups

Quick Start

Start the Server

python -m nc_mcp_server.main

API Reference

Core Functions

Function Description Parameters
list_netcdf_files Lists all NetCDF files in a directory. directory (str): Path to search (default: .).
get_netcdf_info Returns metadata and structure of a NetCDF file (dimensions, variables, attributes). file_path (str): Path to the NetCDF file.
get_variable_data Extracts data from a specific variable (supports slicing and sampling). file_path (str), variable_name (str), slices (dict, optional), max_elements (int, default: 1000).
search_variables Searches variables and attributes for a keyword (case-insensitive). file_path (str), search_term (str).
extract_timeseries Extracts time series data from a spatial variable. file_path (str), variable_name (str), location (dict, optional: spatial coordinates like {"lat": 10, "lon": 20}).

Testing

Run the test suite to verify functionality:

uv run pytest tests/ -v

Tests cover:

  • File discovery logic
  • NetCDF structure parsing
  • Data extraction with slicing
  • Sampling for large datasets
  • Error handling for invalid inputs

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contributing

  1. Fork the repository
  2. Create a feature branch (git checkout -b feature/amazing-feature)
  3. Commit your changes (git commit -m 'Add some amazing feature')
  4. Push to the branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

Links

Author

John Li

README mirrored from the source repository 2 days ago. The original is authoritative.

Questions

About Nc MCP

How do I install Nc MCP?

Run npx nc-mcp, then add the server to your MCP client's configuration. Conduid has recorded 0 installs, so the command is known to work with current clients.

Is Nc MCP safe to use with an AI agent?

Its trust score is 49 out of 100 (fair). It passes 0 of 1 static security checks; the failures are listed above. It has no ConduID identity yet, so agent calls to it are not receipted.

Is Nc MCP still maintained?

The last commit was a year ago, with 0 open issues. That's long enough that you should check whether the maintainer is responding to issues before depending on it.