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Glama

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

Server capabilities have not been inspected yet.

Tools

Functions exposed to the LLM to take actions

NameDescription
mpl_mcp_plot_barchartD

Plots barchart of given datavalues

mpl_mcp_plot_scatterC

Plots scatter chart of given datavalues

mpl_mcp_plot_chartC

Plots line/scatter/bar chart of given datavalues

mpl_mcp_plot_stemD

Plots stem chart of given datavalues

mpl_mcp_plot_stackC

Plots stacked area/bar chart of given datavalues

mpl_mcp_eqn_chartD

Plots mathematical equations

numpy_mcp_numerical_operationD

Do numerical operation like add, sub, mul, div, power, abs, exp, log, sqrt, sin, cos, tan, mean, median, std, var, min, max, argmin, argmax, percentile, dot, matmul, inv, det, eig, solve, svd

numpy_mcp_matlib_operationC

Do matrix operations: rand-mat, zeros, ones, eye, identity, arange, linspace, reshape, flatten, concatenate, transpose, stack

sympy_mcp_algebra_operationC

Do algebraic operations like simplify, expand, factor, collect

sympy_mcp_calculus_operationC

Do calculus operations like diff, integrate, limit, series

sympy_mcp_equation_operationC

Do symbolic equation operations like solve, solveset, linsolve, nonlinsolve

sympy_mcp_matrix_operationC

Do symbolic matrix operations like create, det, inv, rref, eigenvals

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

TDQS

C2.6/5.0

Scored across 12 tools

Disambiguation3/5

The tools are grouped by library (mpl, numpy, sympy) with clear distinctions between groups, but within groups there is significant overlap. For example, mpl_mcp_plot_chart can plot line/scatter/bar charts, making mpl_mcp_plot_barchart and mpl_mcp_plot_scatter partially redundant. Similarly, numpy_mcp_numerical_operation includes matrix operations that overlap with numpy_mcp_matlib_operation, and sympy tools have some functional overlap (e.g., equation solving appears in multiple places). Descriptions help, but agents may struggle to choose between overlapping tools.

Naming Consistency4/5

Naming follows a consistent pattern of library_prefix_mcp_domain_operation (e.g., mpl_mcp_plot_chart, numpy_mcp_matlib_operation). All tools use snake_case consistently. Minor deviations exist, such as 'matlib' vs 'matrix' in numpy tools and slight variations in domain terms (e.g., 'algebra_operation' vs 'calculus_operation'), but the overall structure is predictable and readable.

Tool Count4/5

With 12 tools, the count is reasonable for a mathematical/plotting server covering multiple libraries (matplotlib, numpy, sympy). It's slightly on the higher side but justified by the broad scope. Each tool groups related functionalities, though some consolidation might reduce overlap. The count aligns well with the server's purpose of providing mathematical operations and visualizations.

Completeness4/5

The server covers key mathematical domains: plotting (multiple chart types), numerical operations (basic and advanced), symbolic algebra, calculus, and matrix operations. There are minor gaps, such as no explicit tool for statistical distributions beyond basic stats in numpy, and plotting tools might lack 3D or specialized visualizations. However, core workflows for mathematical analysis and visualization are well-supported, with no major dead ends.

Maintenance

ActivityInactive
ResponsivenessSlow