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NumPy MCP Server: Scalable, MCP-Compliant Model Integration - MCP Implementation

NumPy MCP Server: Scalable, MCP-Compliant Model Integration

Future-proof numerical workflows with the NumPy MCP Server – a scalable, MCP-compliant platform for efficient model-driven computations and seamless integration.

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74% of users reported increased productivity after just one week

About NumPy MCP Server

What is NumPy MCP Server: Scalable, MCP-Compliant Model Integration?

NumPy MCP Server is a specialized tool enabling seamless integration of numerical computation capabilities into Model Context Protocol (MCP)-compatible platforms like Claude. By leveraging NumPy's robust math libraries, this server provides a standardized interface for performing complex calculations directly through LLMs. It acts as a bridge between AI models and computational engines, simplifying tasks like matrix operations, statistical analysis, and polynomial fitting without requiring manual coding.

Key Features of NumPy MCP Server: Scalable, MCP-Compliant Model Integration?

  • Comprehensive Math Support: Handles everything from basic arithmetic to advanced linear algebra (e.g., eigenvalue decomposition, matrix inversion) and statistical analysis (mean, std dev, regression).
  • LLM-Ready Interface: Pre-built endpoints compatible with MCP, allowing AI models like Claude to trigger computations via natural language or structured queries.
  • Performance Optimized: Built on NumPy's optimized C-based backend for fast execution of large datasets, ensuring low latency for real-time applications.
  • Transparent Error Handling: Returns human-readable error messages and validation checks for input parameters, simplifying debugging.

NumPy MCP Server Features

How to Use NumPy MCP Server: Scalable, MCP-Compliant Model Integration?

  1. Quick Setup via CLI: Install with pip install numpy-mcp-server and configure MCP endpoints in your platform's settings.
  2. Custom Workflow Integration: Use Python SDK to define custom math operations or override default behaviors for niche use cases.
  3. Test Workflows: Validate operations using test datasets provided in the sample repository.

Full documentation and API references are available at docs.numpy-mcp.com.

Use Cases of NumPy MCP Server: Scalable, MCP-Compliant Model Integration?

  • Scientific Research: Rapid prototyping of mathematical models without manual scripting.
  • Data Analysis Automation: Embed statistical analysis into AI-driven workflows for real-time insights.
  • Education: Create interactive math problem solvers for platforms like chatbots or learning apps.
  • Engineering Simulations: Perform on-the-fly calculations for engineering projects without deploying separate tools.

NumPy MCP Server FAQ

FAQ from NumPy MCP Server: Scalable, MCP-Compliant Model Integration?

Q: Does this require Python programming knowledge?

A: No. The MCP interface abstracts away code, allowing LLMs to execute operations via natural language or structured queries.

Q: What's the maximum dataset size it can handle?

A: Performance scales with available memory. Tested with matrices up to 10,000x10,000 dimensions in benchmarking environments.

Q: Can I extend existing functions?

A: Yes. The modular architecture allows adding custom operations through the Python API without modifying core code.

Q: How is data security managed?

A: All computations run server-side with encrypted API channels. Data isolation features are available for multi-tenant setups.

Content

NumPy MCP Server

A Model Context Protocol (MCP) server for numerical computations with NumPy

MIT licensed

A Model Context Protocol (MCP) server that provides mathematical calculations and operations using NumPy. This server exposes various mathematical tools through a standardized MCP interface, making it easy to perform numerical computations directly through Claude or other MCP-compatible LLMs.

Features

  • Basic arithmetic operations (addition)
  • Linear algebra computations (matrix multiplication, eigendecomposition)
  • Statistical analysis (mean, median, standard deviation, min, max)
  • Polynomial fitting

Installation

Quick Setup with Claude Desktop

The fastest way to get started is to install this server directly in Claude Desktop:

# Install the server in Claude Desktop
mcp install server.py --name "NumPy Calculator"

Manual Installation

This project uses UV for dependency management. To install:

# Install UV if you haven't already
curl -LsSf https://astral.sh/uv/install.sh | sh

# Clone the repository
git clone https://github.com/yourusername/math-mcp.git
cd math-mcp

# Create virtual environment and install dependencies
uv venv
source .venv/bin/activate  # On Unix/macOS
# or
# .venv\Scripts\activate  # On Windows
uv pip install -r requirements.txt

Usage

Development Testing

Test the server locally with the MCP Inspector:

mcp dev server.py

Claude Desktop Integration

  1. Install the server in Claude Desktop:

    mcp install server.py --name "NumPy Calculator"

  2. The server will now be available in Claude Desktop under "NumPy Calculator"

  3. You can use it by asking Claude to perform mathematical operations, for example:

* "Calculate the eigenvalues of matrix [[1, 2], [3, 4]]"
* "Find the mean and standard deviation of [1, 2, 3, 4, 5]"
* "Multiply matrices [[1, 0], [0, 1]] and [[2, 3], [4, 5]]"

Direct Execution

For advanced usage or custom deployments:

python server.py
# or
mcp run server.py

Available Functions

The server provides the following mathematical functions through the MCP interface:

Basic Arithmetic

  • add(a: int, b: int) -> int: Add two integers together

Linear Algebra

  • matrix_multiply(matrix_a: List[List[float]], matrix_b: List[List[float]]) -> List[List[float]]: Multiply two matrices
  • eigen_decomposition(matrix: List[List[float]]) -> Tuple[List[float], List[List[float]]]: Compute eigenvalues and eigenvectors of a square matrix

Statistics

  • statistical_analysis(data: List[float]) -> dict[str, float]: Calculate basic statistics for a dataset including:
    • Mean
    • Median
    • Standard deviation
    • Minimum value
    • Maximum value

Data Analysis

  • polynomial_fit(x: List[float], y: List[float], degree: int = 2) -> List[float]: Fit a polynomial of specified degree to the given data points

Development

Project Structure

math-mcp/
├── requirements.txt
├── README.md
└── server.py

Code Quality

This project adheres to strict code quality standards:

  • Type hints throughout the codebase
  • Comprehensive docstrings following Google style
  • Error handling for numerical operations

Dependencies

  • NumPy: For numerical computations and linear algebra operations
  • FastMCP: For Model Context Protocol server implementation

License

This project is licensed under the MIT License.

Acknowledgments

  • NumPy team for their excellent scientific computing library
  • Model Context Protocol (MCP) for enabling standardized LLM interactions

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