Differentiation MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
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
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| differentiate_symbolicA | Compute symbolic derivatives of mathematical expressions using SymPy |
| differentiate_numericalC | Compute numerical derivatives using autograd for specific function values |
| partial_derivativesC | Compute partial derivatives for multivariable functions |
| gradient_vectorB | Compute the gradient vector of a multivariable function |
| chain_ruleB | Apply the chain rule for composite functions |
| implicit_differentiationB | Perform implicit differentiation for equations of the form F(x,y) = 0 |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| differentiation-help | Get help with differentiation concepts and choose the right tool |
| calculus-problem-solver | Structured approach to solving calculus problems involving differentiation |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 6 tools
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.
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.
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.
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.