"Linear" matching MCP servers:

A symbolic mathematics MCP server supporting calculus, linear algebra, number theory, statistics, and unit conversion via natural language.

Enables reliable engineering and scientific computation through tools for exact arithmetic, unit-aware formulas, calculus, linear algebra, statistics, uncertainty propagation, and physical constants, all executed safely in reproducible subprocesses.

A Model Context Protocol server providing mathematical and scientific computing capabilities via NumPy, pandas, SciPy, and Matplotlib, with 19 tools for linear algebra, statistics, numerical methods, data processing, and plotting.

A lightweight MCP server for linear-static structural analysis of bridges and building frames, providing tools to build 3D beam/truss models, analyze with built-in or CalculiX solvers, and export to FreeCAD.

A universal mathematics MCP server that gives LLM clients full access to SageMath for symbolic calculus, number theory, linear algebra, and more, with persistent state across tool calls.

Enables agents to forecast numerical trends with zero-dependency Holt linear exponential smoothing, multi-step horizons, variance confidence bands, and supporting statistical anomaly detection, regression, hypothesis testing, and PCA.

Enables agent sessions to drive OriginPro via COM for plotting XY data, performing linear fits with R2, and exporting PNG images.

Provides econometric modeling capabilities through R, enabling AI assistants to perform sophisticated analyses including linear regression, panel data models, instrumental variables regression, and diagnostic tests.

Enables mathematical and scientific computations including linear algebra operations (matrices, eigenvalues, decompositions), vector calculus (gradients, curl, divergence), and visualization of vector fields and functions.

Enables agents to run zero-dependency statistical analysis natively over MCP, including Power Iteration PCA/SVD dimensionality reduction, Holt linear forecasting, modified Z-score and Tukey IQR anomaly detection, Welch's t-test, and multivariate gradient-descent regression on numerical data streams. Projects high-dimensional feature vectors into principal component representations without any external libraries.

Enables solving linear programming (LP) and mixed-integer linear programming (MILP) optimization problems through natural language, with built-in simplex and branch-and-cut solvers plus infeasibility diagnostics. Includes optional OR-Tools fallback for larger problems and supports parsing optimization problems from natural language descriptions.

Provides linear programming (LP), mixed-integer programming (MIP), and quadratic programming (QP) optimization capabilities using the HiGHS solver, enabling AI assistants to solve complex optimization problems like production planning, logistics, and portfolio optimization.

Enables AI applications to solve optimization problems including linear programming, mixed-integer programming, and quadratic programming using the Gurobi solver installed on the local device.

Provides a local, agent-maintained product context layer over MCP, serving structured strategy, competitor, customer feedback, and feature inventory data to any AI tool such as Claude, Cursor, or ChatGPT, with local data storage, bring-your-own-LLM support, and weekly evidence-based roadmap reviews that can sync suggestions to Jira or Linear.

Enables AI assistants to solve linear, nonlinear, and integer optimization models with a locally installed LINGO 11 solver and read the results back as structured data — status, objective, variable values, reduced costs, row slacks, and dual prices. It also runs arbitrary LINGO command-window analyses, reports the detected installation and license status, and lists the bundled sample models, correctly flagging infeasible or unbounded models instead of inventing an optimum.

Enables an agent to run ten ready-made research and monitoring workflows — competitor digests, cited reports, paper alerts, market maps, regulatory and due-diligence watches — pulling from Firecrawl, Tavily, GitHub and landing results in Notion, Slack, Google Docs, Sheets and Linear. Fields are filed with the user's own keys held in the operating system keychain, and every action that creates, sends or changes anything is shown and approved first.

Enables agents to run statistics and data-science computations over numerical streams using only the Python standard library, including Z-score/IQR anomaly detection, Holt linear forecasting, Welch's t-test hypothesis evaluation, gradient-descent multivariate regression, and power-iteration SVD/PCA dimensionality reduction. Exposes these capabilities over JSON-RPC 2.0 stdio so clients like Claude Desktop, Cursor, and Windsurf can project high-dimensional feature vectors into principal components without any external dependencies.