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The MCP server exposes SymPy's symbolic expression API, allowing clients to define variables, construct equations, and request symbolic solutions without numerical approximation. Operations are executed server-side and results returned as symbolic expressions or simplified forms.","intents":["I need to solve algebraic equations symbolically and get exact answers, not floating-point approximations","I want to simplify complex mathematical expressions programmatically","I need to perform polynomial operations like factoring or expanding expressions","I want to compute derivatives and integrals symbolically for calculus problems"],"best_for":["mathematicians and scientists building computational workflows","educational tools requiring exact symbolic solutions","research applications needing symbolic manipulation in automated pipelines"],"limitations":["symbolic computation can be computationally expensive for complex expressions, causing timeouts on deeply nested operations","no support for custom symbolic domains or non-standard algebraic structures","limited to SymPy's built-in symbolic capabilities — cannot extend with custom algebra rules"],"requires":["Python 3.8+","SymPy library installed on MCP server","MCP client capable of sending structured mathematical expressions"],"input_types":["symbolic expressions (as strings or structured notation)","variable definitions","equation specifications"],"output_types":["simplified symbolic expressions","solution sets","factored or expanded forms","derivative/integral expressions"],"categories":["data-processing-analysis","mathematical-computation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_abhiphile-fermat-mcp__cap_1","uri":"capability://data.processing.analysis.numerical.computation.with.numpy","name":"numerical-computation-with-numpy","description":"Execute numerical computations using NumPy arrays and linear algebra operations, including matrix operations, statistical calculations, and numerical transformations. The server wraps NumPy's vectorized operations and exposes them through MCP function calls, handling array serialization/deserialization and returning results as JSON-compatible numeric structures. Supports batch operations on multi-dimensional arrays.","intents":["I need to perform fast numerical computations on large arrays without writing Python code directly","I want to do linear algebra operations like matrix multiplication, eigenvalue decomposition, or SVD","I need statistical calculations like mean, variance, correlation matrices across datasets","I want to transform and manipulate numerical data programmatically in my LLM workflow"],"best_for":["data scientists building automated analysis pipelines","ML engineers needing numerical preprocessing in agent workflows","developers creating scientific computing applications with LLM integration"],"limitations":["array serialization to JSON adds overhead for very large matrices (>10K x 10K elements)","no GPU acceleration — all computations run on CPU through NumPy","limited to NumPy's built-in operations; custom BLAS/LAPACK bindings not exposed"],"requires":["Python 3.8+","NumPy library installed on MCP server","sufficient memory for array operations"],"input_types":["numeric arrays (as JSON lists or structured formats)","matrix specifications","statistical data"],"output_types":["numeric arrays","scalar results","statistical summaries","matrix decompositions"],"categories":["data-processing-analysis","mathematical-computation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_abhiphile-fermat-mcp__cap_10","uri":"capability://image.visual.custom.plot.styling.and.formatting","name":"custom-plot-styling-and-formatting","description":"Customizes plot appearance through Matplotlib's styling API, supporting color schemes, line styles, markers, fonts, legends, grid options, and axis formatting. Accepts plot objects and styling specifications, applies Matplotlib formatting functions, and returns styled plots. Supports both programmatic styling and predefined style templates.","intents":["I want to apply consistent styling and color schemes to plots","I need to customize fonts, labels, and legends for publication quality","I want to apply grid, tick, and axis formatting","I need to create plots matching specific style guidelines"],"best_for":["researchers creating publication-ready figures","educators building visually consistent educational materials","data visualization specialists ensuring brand consistency"],"limitations":["Complex styling operations may require multiple tool calls","Some advanced Matplotlib features not exposed through simplified tool interface","Style application order matters; incorrect sequencing can override settings"],"requires":["Python 3.8+","Matplotlib library","MCP client"],"input_types":["plot objects or specifications","styling parameters (colors, fonts, sizes)","style template names"],"output_types":["styled plot images","plot configuration objects"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_abhiphile-fermat-mcp__cap_2","uri":"capability://data.processing.analysis.calculus.operations.symbolic.and.numerical","name":"calculus-operations-symbolic-and-numerical","description":"Compute derivatives, integrals, limits, and series expansions using SymPy for symbolic calculus and NumPy for numerical differentiation/integration. The server routes requests to appropriate backends based on operation type — symbolic operations use SymPy's calculus module, while numerical integration uses scipy.integrate. Results include both symbolic expressions and numerical evaluations where applicable.","intents":["I need to compute derivatives of complex functions symbolically","I want to evaluate definite integrals numerically or symbolically","I need to find limits of functions as variables approach specific values","I want to compute Taylor series expansions for approximation"],"best_for":["physics and engineering simulations requiring calculus operations","educational platforms teaching calculus concepts","optimization algorithms needing gradient computations"],"limitations":["symbolic integration may fail or timeout for transcendental functions without closed-form solutions","numerical integration accuracy depends on function smoothness and chosen algorithm parameters","no support for multidimensional calculus operations like divergence or curl"],"requires":["Python 3.8+","SymPy and SciPy installed on MCP server","function definitions in symbolic or callable form"],"input_types":["symbolic expressions","function definitions","variable specifications","integration bounds"],"output_types":["derivative expressions","integral results (symbolic or numerical)","limit values","series coefficients"],"categories":["data-processing-analysis","mathematical-computation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_abhiphile-fermat-mcp__cap_3","uri":"capability://data.processing.analysis.statistical.analysis.and.aggregation","name":"statistical-analysis-and-aggregation","description":"Perform statistical computations including descriptive statistics (mean, median, variance, skewness), correlation analysis, hypothesis testing, and probability distributions using NumPy and SciPy.stats. The server accepts datasets as arrays and returns statistical summaries, correlation matrices, and test results with p-values. Supports both parametric and non-parametric statistical tests.","intents":["I need to compute descriptive statistics on datasets to understand their distribution","I want to calculate correlation matrices to identify relationships between variables","I need to perform hypothesis tests to validate statistical assumptions","I want to work with probability distributions for modeling and simulation"],"best_for":["data analysts building automated statistical reporting","researchers validating experimental results programmatically","business intelligence systems computing KPIs and metrics"],"limitations":["assumes data is already cleaned and formatted; no built-in handling of missing values or outliers","statistical tests require sufficient sample sizes; results unreliable for small datasets (<30 samples)","no support for time-series specific statistics like autocorrelation or ARIMA"],"requires":["Python 3.8+","NumPy and SciPy installed on MCP server","numeric datasets in array format"],"input_types":["numeric arrays","dataset specifications","test parameters"],"output_types":["statistical summaries (JSON objects)","correlation matrices","test results with p-values","distribution parameters"],"categories":["data-processing-analysis","mathematical-computation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_abhiphile-fermat-mcp__cap_4","uri":"capability://image.visual.2d.plot.generation.matplotlib","name":"2d-plot-generation-matplotlib","description":"Generate 2D plots including line plots, scatter plots, histograms, bar charts, and heatmaps using Matplotlib. The server accepts plot specifications (data, axes labels, plot type) and returns rendered images as PNG or SVG. Supports customization of colors, markers, legends, and styling. Generated plots are serialized as base64-encoded images or file paths for client consumption.","intents":["I need to visualize numerical data as line plots or scatter plots to identify trends","I want to create histograms to understand data distributions","I need to generate publication-quality plots with custom styling and labels","I want to create heatmaps to visualize correlation matrices or 2D data"],"best_for":["data scientists creating automated reporting dashboards","researchers generating figures for papers programmatically","educational tools visualizing mathematical concepts"],"limitations":["Matplotlib rendering can be slow for plots with >100K data points","no interactive plot generation — outputs are static images","limited 3D plotting capabilities; 3D plots may have rendering artifacts"],"requires":["Python 3.8+","Matplotlib installed on MCP server","numeric data in array format"],"input_types":["numeric arrays (x, y data)","plot specifications (type, labels, styling)","categorical data for bar/histogram plots"],"output_types":["PNG images","SVG images","base64-encoded image data"],"categories":["image-visual","data-visualization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_abhiphile-fermat-mcp__cap_5","uri":"capability://image.visual.3d.plot.generation.matplotlib","name":"3d-plot-generation-matplotlib","description":"Generate 3D surface plots, scatter plots, and wireframe visualizations using Matplotlib's mplot3d toolkit. The server accepts 3D data specifications and returns rendered 3D plots as images with configurable viewing angles and projections. Supports surface plots from function definitions or data grids, and 3D scatter plots for point cloud visualization.","intents":["I need to visualize 3D functions or surfaces to understand their behavior","I want to create 3D scatter plots to explore multi-dimensional data","I need to generate 3D wireframe plots for mathematical surfaces","I want to visualize point clouds or spatial data in three dimensions"],"best_for":["scientists and engineers visualizing 3D spatial data","mathematicians exploring 3D function behavior","researchers creating 3D visualizations for presentations"],"limitations":["3D rendering is computationally expensive; large datasets (>50K points) may timeout","no interactive 3D rotation — viewing angle must be specified at plot creation time","3D plots with transparency or complex lighting may have rendering artifacts"],"requires":["Python 3.8+","Matplotlib with mplot3d toolkit installed","3D data in grid or point format"],"input_types":["3D coordinate arrays (x, y, z)","function definitions for surface generation","viewing angle specifications"],"output_types":["PNG images","SVG images","base64-encoded image data"],"categories":["image-visual","data-visualization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_abhiphile-fermat-mcp__cap_6","uri":"capability://tool.use.integration.mcp.function.calling.interface","name":"mcp-function-calling-interface","description":"Expose all mathematical and plotting operations through the Model Context Protocol (MCP) as callable functions with typed schemas. The server implements MCP's tool/function interface, allowing LLM clients to discover available operations, inspect parameter schemas, and invoke computations with automatic argument validation and error handling. Results are returned as structured JSON responses compatible with LLM processing.","intents":["I want my LLM agent to discover and call mathematical functions without hardcoding function names","I need structured function schemas so my LLM can understand parameter types and constraints","I want automatic error handling and validation when calling mathematical operations","I need to integrate mathematical computation into my LLM agent's tool ecosystem"],"best_for":["LLM agent developers building multi-tool orchestration systems","teams using Claude or other MCP-compatible LLMs","developers building AI-driven scientific computing workflows"],"limitations":["MCP protocol overhead adds ~50-100ms latency per function call","function schemas must be manually maintained as operations are added","no built-in caching of computation results across multiple calls"],"requires":["MCP-compatible LLM client (Claude, etc.)","MCP server running Fermat","Python 3.8+ on server"],"input_types":["MCP function call requests with typed parameters"],"output_types":["structured JSON responses","error messages with diagnostic information"],"categories":["tool-use-integration","mcp-protocol"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_abhiphile-fermat-mcp__cap_7","uri":"capability://automation.workflow.batch.mathematical.operation.execution","name":"batch-mathematical-operation-execution","description":"Execute multiple mathematical operations in sequence or parallel through a single MCP request, with support for chaining results between operations. The server accepts a batch specification defining operation order, data dependencies, and parameter mappings, then executes the batch and returns all results in a single response. Enables complex multi-step mathematical workflows without repeated MCP round-trips.","intents":["I need to run multiple mathematical operations in sequence where later operations depend on earlier results","I want to reduce latency by batching multiple computations into a single request","I need to compute multiple statistics or plots from the same dataset efficiently","I want to define complex mathematical workflows as a single batch operation"],"best_for":["LLM agents executing complex multi-step mathematical workflows","data analysis pipelines requiring sequential transformations","scientific computing applications with interdependent calculations"],"limitations":["batch execution is sequential by default; no automatic parallelization across independent operations","error in one operation halts the batch unless explicit error handling is configured","batch size is limited by server memory; very large batches may timeout"],"requires":["MCP server with batch execution support","operation definitions with explicit data dependencies"],"input_types":["batch specifications (array of operations with dependencies)"],"output_types":["batch results (array of operation outputs in order)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_abhiphile-fermat-mcp__cap_8","uri":"capability://safety.moderation.error.handling.and.computation.diagnostics","name":"error-handling-and-computation-diagnostics","description":"Provide detailed error messages, computation diagnostics, and fallback strategies when mathematical operations fail or produce unexpected results. The server catches computation errors (e.g., singular matrices, convergence failures) and returns structured error responses with diagnostic information, suggested corrections, and alternative approaches. Includes warnings for numerical instability or precision loss.","intents":["I need to understand why a mathematical operation failed and how to fix it","I want warnings about numerical precision loss or instability in computations","I need suggestions for alternative approaches when a computation fails","I want diagnostic information to debug mathematical workflows"],"best_for":["developers debugging mathematical workflows","LLM agents that need to recover from computation failures","educational tools teaching mathematical problem-solving"],"limitations":["diagnostic messages are generated heuristically; may not always identify root cause","fallback suggestions require domain knowledge and may not apply to all use cases","no interactive debugging — diagnostics are returned as static messages"],"requires":["MCP server with error handling implemented"],"input_types":["failed computation requests"],"output_types":["error messages","diagnostic information","suggested corrections"],"categories":["safety-moderation","debugging"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_abhiphile-fermat-mcp__cap_9","uri":"capability://data.processing.analysis.equation.solving.and.root.finding","name":"equation-solving-and-root-finding","description":"Solves equations and finds roots of functions using SymPy's symbolic solver and numerical root-finding methods. Accepts equations (symbolic or string format), variable specifications, and optional initial guesses, then applies appropriate solving algorithms. Returns exact symbolic solutions when available, with numerical fallbacks for transcendental equations. Supports systems of equations and polynomial root finding.","intents":["I need to find roots of polynomial equations exactly","I want to solve systems of nonlinear equations","I need to find numerical roots of transcendental functions","I want to solve equations with multiple variables"],"best_for":["mathematicians solving equations in automated workflows","engineers finding solutions to nonlinear systems","researchers analyzing equation solutions programmatically"],"limitations":["Symbolic solving fails for many transcendental equations; numerical methods required","Polynomial equations of degree >4 may not have closed-form solutions","Numerical root finding requires good initial guesses for convergence"],"requires":["Python 3.8+","SymPy library","SciPy for numerical root finding","MCP client"],"input_types":["equation expressions (symbolic or string)","variable specifications","initial guesses (for numerical methods)"],"output_types":["symbolic solutions","numerical roots","solution sets","convergence information"],"categories":["data-processing-analysis","mathematical-computation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":36,"verified":false,"data_access_risk":"moderate","permissions":["Python 3.8+","SymPy library installed on MCP server","MCP client capable of sending structured mathematical expressions","NumPy library installed on MCP server","sufficient memory for array operations","Matplotlib library","MCP client","SymPy and SciPy installed on MCP server","function definitions in symbolic or callable form","NumPy and SciPy installed on MCP server"],"failure_modes":["symbolic computation can be computationally expensive for complex expressions, causing timeouts on deeply nested operations","no support for custom symbolic domains or non-standard algebraic structures","limited to SymPy's built-in symbolic capabilities — cannot extend with custom algebra rules","array serialization to JSON adds overhead for very large matrices (>10K x 10K elements)","no GPU acceleration — all computations run on CPU through NumPy","limited to NumPy's built-in operations; custom BLAS/LAPACK bindings not exposed","Complex styling operations may require multiple tool calls","Some advanced Matplotlib features not exposed through simplified tool interface","Style application order matters; incorrect sequencing can override settings","symbolic integration may fail or timeout for transcendental functions without closed-form solutions","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.57,"ecosystem":0.48999999999999994,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.061Z","last_scraped_at":"2026-05-03T15:19:15.091Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=abhiphile-fermat-mcp","compare_url":"https://unfragile.ai/compare?artifact=abhiphile-fermat-mcp"}},"signature":"ZOmJQmrsViZhmczvN/QAwyEZqLNmGTMG73l0TkQVz2gXBrAXPrKU74oX44aajNfTtUfNi3Cqww89mlm2Gs7MDQ==","signedAt":"2026-06-22T04:27:23.058Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/abhiphile-fermat-mcp","artifact":"https://unfragile.ai/abhiphile-fermat-mcp","verify":"https://unfragile.ai/api/v1/verify?slug=abhiphile-fermat-mcp","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}