Skip to main content
Glama
ShawneilRodrigues

Differentiation MCP Server

by ShawneilRodrigues
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Related Servers

Alternatives to Differentiation MCP Server

No user-submitted related servers found.

    Related Servers

    • ReasonForge

      RoyCoding8
      A
      license
      Not graded
      quality
      D
      maintenance
      Provides a suite of deterministic math tools powered by SymPy to handle algebra, calculus, linear algebra, and statistics via the Model Context Protocol. It enables smaller language models to delegate complex computations to a verified symbolic backend for accurate and reliable results.
      Apache 2.0

    • pycodemath

      cybersora9
      A
      license
      Not graded
      quality
      A
      maintenance
      Provides a token-efficient exact math engine for AI agents, enabling computation of derivatives, integrals, equations, and optimized Python/NumPy code via a single MCP tool.
      4
      MIT

    • nsforge-mcp

      u9401066
      A
      license
      B
      quality
      B
      maintenance
      Enables creation of new mathematical formulas through symbolic derivation, verification, and step-by-step control, leveraging SymPy for computation and storing results with provenance.
      82
      4
      Apache 2.0

    • Math MCP Server

      codeprimate
      A
      license
      Not graded
      quality
      C
      maintenance
      Provides powerful symbolic mathematics and statistical analysis capabilities, enabling users to solve equations, compute derivatives and integrals, perform statistical tests, and analyze data through natural language requests.
      4
      MIT
    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

    B3.4/5.0

    Scored across 6 tools

    Disambiguation4/5

    Most tools have clearly distinct purposes: symbolic vs. numerical differentiation are separated by method, and chain rule/implicit differentiation cover specific techniques. However, partial_derivatives and gradient_vector are closely related since a gradient is essentially the vector of all partial derivatives, which could cause some misselection.

    Naming Consistency3/5

    Two tools follow a clear verb_noun pattern (differentiate_symbolic, differentiate_numerical), but the remaining four are descriptive noun phrases like partial_derivatives, gradient_vector, chain_rule, and implicit_differentiation. The naming is readable and consistently snake_case, but the structural conventions are mixed.

    Tool Count5/5

    Six tools is a well-scoped count for a differentiation-focused server. Each tool covers a meaningful aspect of the domain without unnecessary redundancy or bloat.

    Completeness4/5

    The toolset covers symbolic, numerical, partial, gradient, chain rule, and implicit differentiation, which addresses the core domain well. Minor gaps such as higher-order derivatives or directional derivatives could be added, but they are not obvious dead ends for typical differentiation workflows.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources