Sympy
Sympy-MCP is an MCP server that enables autonomous symbolic manipulations, equation solving, and complex mathematical operations.
Features
- 🧮 Algebraic equation solving and system solving
- 🔄 Integration and differentiation
- 🌀 Vector and tensor calculus operations
- 🚀 Support for general relativity calculations
- 📊 Matrix operations including determinants, inverses, and eigenvalues
- ⚖️ Unit conversion and simplification
- 📐 Coordinate system creation and vector field manipulations
Use Cases
- Data scientists can use Sympy-MCP to solve complex equations and perform symbolic manipulations in their analysis pipelines without leaving their AI-assisted coding environment.
- Physics researchers can leverage the general relativity tools to perform tensor calculations and solve equations in curved spacetime.
- Engineering students can utilize the differential equation solvers to tackle complex problems in dynamical systems and control theory.
- Machine learning engineers can employ the matrix operations for algorithm development and optimization tasks.
How to use it
Installation:
- Clone the repository:
git clone https://github.com/sdiehl/sympy-mcp.git - Navigate to the project directory:
cd sympy-mcp - Install dependencies using uv:
uv sync
To install and run the server:
- Install the server to Claude Desktop:
uv run mcp install server.py - Run the server:
uv run mcp run server.py
For a standalone version:
uv run --with https://github.com/sdiehl/sympy-mcp/releases/download/0.1/sympy_mcp-0.1.0-py3-none-any.whl python server.py
For general relativity calculations, install the einsteinpy library:
uv sync --group relativityAvailable Tools:
- Variable Introduction (intro): Introduces variables with specified assumptions
- Expression Parser (introduce_expression): Parses and stores mathematical expressions
- Algebraic Solver (solve_algebraically): Solves equations algebraically
- ODE Solver (dsolve_ode): Solves ordinary differential equations
- PDE Solver (pdsolve_pde): Solves partial differential equations
- Tensor Calculator (calculate_tensor): Calculates tensors from metrics
- Integration (integrate_expression): Integrates expressions
- Differentiation (differentiate_expression): Differentiates expressions
- Vector Field Operations: Create fields and calculate curl, divergence, and gradient
- Matrix Operations: Create matrices and perform determinant, inverse, and eigenvalue calculations
FAQS
Q: Can Sympy-MCP handle symbolic integration of complex functions?
A: Yes, Sympy-MCP leverages SymPy’s integration capabilities, which can handle a wide range of complex integrals, including those involving special functions and trigonometric expressions.
Q: Is it possible to solve systems of nonlinear equations using Sympy-MCP?
A: Absolutely. The server provides a nonlinear solver tool that can tackle systems of nonlinear equations, though the complexity of the system may affect the solving time and feasibility.
Q: Can I use Sympy-MCP for tensor calculations in general relativity?
A: Yes, Sympy-MCP offers robust support for general relativity calculations. You can create custom metrics, compute various tensors (Ricci, Einstein, Weyl), and perform operations relevant to curved spacetime physics.
Q: How does Sympy-MCP handle units and dimensional analysis?
A: The server includes unit conversion and simplification tools. You can convert quantities between different units and simplify expressions involving physical quantities, ensuring dimensional consistency in your calculations.
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FAQs
Q: What exactly is the Model Context Protocol (MCP)?
A: MCP is an open standard, like a common language, that lets AI applications (clients) and external data sources or tools (servers) talk to each other. It helps AI models get the context (data, instructions, tools) they need from outside systems to give more accurate and relevant responses. Think of it as a universal adapter for AI connections.
Q: How is MCP different from OpenAI's function calling or plugins?
A: While OpenAI's tools allow models to use specific external functions, MCP is a broader, open standard. It covers not just tool use, but also providing structured data (Resources) and instruction templates (Prompts) as context. Being an open standard means it's not tied to one company's models or platform. OpenAI has even started adopting MCP in its Agents SDK.
Q: Can I use MCP with frameworks like LangChain?
A: Yes, MCP is designed to complement frameworks like LangChain or LlamaIndex. Instead of relying solely on custom connectors within these frameworks, you can use MCP as a standardized bridge to connect to various tools and data sources. There's potential for interoperability, like converting MCP tools into LangChain tools.
Q: Why was MCP created? What problem does it solve?
A: It was created because large language models often lack real-time information and connecting them to external data/tools required custom, complex integrations for each pair. MCP solves this by providing a standard way to connect, reducing development time, complexity, and cost, and enabling better interoperability between different AI models and tools.
Q: Is MCP secure? What are the main risks?
A: Security is a major consideration. While MCP includes principles like user consent and control, risks exist. These include potential server compromises leading to token theft, indirect prompt injection attacks, excessive permissions, context data leakage, session hijacking, and vulnerabilities in server implementations. Implementing robust security measures like OAuth 2.1, TLS, strict permissions, and monitoring is crucial.
Q: Who is behind MCP?
A: MCP was initially developed and open-sourced by Anthropic. However, it's an open standard with active contributions from the community, including companies like Microsoft and VMware Tanzu who maintain official SDKs.
