Skip to main content
Glama
amichae2

Math MCP Server

by amichae2
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
simplifyA
Simplify a mathematical expression algebraically.

Args:
    expression: A SymPy-compatible expression string.
                Examples: "(x**2 - 1)/(x - 1)", "sin(x)**2 + cos(x)**2"

Returns:
    Standard result dictionary containing the simplified expression.
expandA
Expand a mathematical expression.

Args:
    expression: SymPy-compatible expression.
                Examples: "(x+1)**3", "(a+b)*(c+d)"

Returns:
    Standard result dictionary containing the expanded expression.
factorB
Factor a mathematical expression over the rational numbers.

Args:
    expression: SymPy-compatible polynomial or rational expression.
                Examples: "x**3 - 6*x**2 + 11*x - 6", "x**4 - 1"

Returns:
    Standard result dictionary containing the factored expression.
solveA
Solve equations or systems of equations symbolically.

Args:
    equation: Equation string or comma-separated system.
    variables: Optional variable names to solve for.
    domain: "real", "complex", "integer", or "positive".
    show_steps: Whether to include a short human-readable explanation.
symbolic_integrateC

Compute symbolic indefinite or definite integrals.

symbolic_diffC

Compute symbolic derivatives of arbitrary order.

limitC

Compute limits of expressions.

seriesC

Compute Taylor or Laurent series expansions.

symbolic_sumC

Compute symbolic finite or infinite summations.

symbolic_productC

Compute symbolic finite or infinite products.

dsolveC

Solve ordinary differential equations symbolically.

laplace_transformC

Compute the Laplace transform of a time-domain expression.

inverse_laplaceC

Compute the inverse Laplace transform.

numerical_integrateC

Numerically integrate a scalar function.

find_rootC

Find a root of a scalar function numerically.

ode_solveC

Solve an initial value problem numerically.

interpolateC

Interpolate scattered one-dimensional data.

curve_fitC

Fit a nonlinear model to data with least squares.

matrix_multiplyC

Multiply two matrices A @ B.

solve_linearC

Solve a square linear system Ax = b.

lstsqC

Compute a least-squares solution to Ax ≈ b.

matrix_decomposeC

Compute standard matrix decompositions.

eigen_decompC

Compute eigenvalues and optionally eigenvectors.

matrix_infoC

Compute structural and numerical information about a matrix.

describeC

Compute comprehensive descriptive statistics for a dataset.

distributionC

Evaluate, fit, and sample probability distributions.

hypothesis_testC

Perform a named statistical hypothesis test.

regressionC

Perform regression analysis for linear, polynomial, or logistic models.

bootstrapC

Estimate bootstrap confidence intervals and uncertainty.

random_sampleC

Generate random samples from NumPy distributions.

factor_intC

Factor an integer into prime factors.

is_primeC

Test whether an integer is prime.

primes_rangeC

List primes in a range or generate the first N primes.

gcd_lcmC

Compute GCD, LCM, or Euclidean algorithm steps.

modularD

Perform modular arithmetic operations.

combinatoricsC

Evaluate combinatorial number sequences and counting functions.

partitionsC

Count or list integer partitions.

diophantineC

Solve Diophantine equations over the integers.

graph_createC

Create a graph from a named specification, edge list, or adjacency matrix.

shortest_pathC

Compute shortest paths on an edge-list graph.

spanning_treeC

Compute a minimum or maximum spanning tree.

graph_metricsC

Compute common graph-theoretic summary metrics.

max_flowC

Compute maximum flow in a capacitated directed graph.

gpu_matrix_multiplyC

Multiply two matrices with GPU acceleration when beneficial.

gpu_fftC

Compute FFTs on GPU when beneficial, otherwise on CPU.

gpu_eigen_batchC

Compute batched eigen decompositions with GPU fallback.

gpu_solveC

Solve batched linear systems with GPU fallback.

to_latexC

Convert a mathematical expression to LaTeX and render it.

render_mathB

Render arbitrary LaTeX math to a PNG data URI.

plot_functionC

Plot one or more functions as 2D curves.

plot_implicitC

Plot implicit equations or inequalities in x and y.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

C2.5/5.0

Scored across 51 tools

Disambiguation3/5

Many tools have distinct purposes, but there is overlap between symbolic and numeric solvers (solve, solve_linear, find_root, dsolve, ode_solve) and between fitting tools (regression, curve_fit, lstsq). Descriptions often clarify, but an agent could still misselect.

Naming Consistency3/5

Tool names follow a mix of patterns: single verbs (factor, simplify), verb_noun (factor_int, find_root), and noun_verb (graph_create, matrix_decompose). Some use abbreviations (gpu_fft) or adjectives (max_flow). The inconsistency is notable across 51 tools.

Tool Count2/5

With 51 tools, the set is large and contains redundancies (e.g., multiple ODE solvers, multiple rendering tools). While the domain is broad, the granularity could be reduced for better usability.

Completeness4/5

The tool set covers a wide range of mathematical domains: symbolic, numeric, statistical, linear algebra, graph theory, number theory, transforms, and plotting. Minor gaps exist (e.g., optimization, special functions) but overall it's very comprehensive.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources