Complex Mathematics Engine

Complex Mathematics Engine: Execute mathematical expressions using SymPy (symbolic), NumPy (numerical), or SciPy (scientific). Supports arithmetic, calculus, linear algebra, statistics, and equation solving with automatic backend detection. Use when an agent needs complex mathematics engine, calculus, solve derivative, calculate integral, find limit of function, calculate, expression, engine hint through AgentPMT-hosted remote tool calls. Discovery terms: complex mathematics engine, calculus.

AgentPMT@agentpmt

Install

openclaw skills install @agentpmt/complex-mathematics-engine

Complex Mathematics Engine

Freshness

Last updated: 2026-06-10.

If the current date is more than 7 days after the last updated date, reinstall this skill from skills.sh or ClawHub before relying on endpoints, schemas, setup steps, or examples.

What This Tool Does

A universal math engine that intelligently executes mathematical and scientific expressions. An agent can submit a single expression string to solve a wide range of problems without needing to select a specific engine, ranging from simple arithmetic to advanced symbolic mathematics, numerical array operations, and scientific computing. It integrates three powerful computation backends: SymPy for symbolic mathematics including differentiation, integration, limits, series expansions, equation solving, and algebraic simplification; NumPy for numerical operations on arrays and matrices including linear algebra, element-wise operations, and statistical aggregations; and SciPy for scientific computing including probability distributions, optimization, curve fitting, special functions, and numerical integration. The engine automatically detects the appropriate back end based on expression syntax, or users can specify a preferred engine explicitly. Expressions support intuitive syntax including Unicode math symbols like π, ∞, and √ which are automatically converted, as well as caret notation for exponentiation. Symbolic results preserve variables and can be further manipulated, while numerical results are returned as JSON-serializable values with full precision. Built-in security validation prevents code injection while allowing access to a comprehensive library of mathematical functions. Results include execution timing, the engine used, and metadata about the computation including detected variables for symbolic expressions.

Product Instructions

Complex Mathematics Engine (009) - Instructions

Overview

Evaluate mathematical expressions using three powerful computation engines:

SymPy — Symbolic math (calculus, algebra, equation solving)
NumPy — Numerical computation (arrays, linear algebra, statistics)
SciPy — Scientific computing (statistics distributions, optimization, special functions)

Action: `calculate`

Parameters

Parameter	Type	Required	Description
`expression`	string	Yes	Mathematical expression to compute. Max 50,000 characters.
`engine_hint`	string	No	Force a specific engine: `auto` (default), `sympy`, `numpy`, `scipy`.

Engine Auto-Detection

When engine_hint is auto (default), the engine is chosen based on the expression:

SciPy — Expressions containing scipy., stats., optimize., special., interpolate., integrate., curve_fit, least_squares
NumPy — Expressions containing np., numpy., array, zeros, ones, eye, linspace, arange, mean, std, dot, linalg.
SymPy — Expressions containing diff, integrate, limit, solve, simplify, expand, factor (and is the default fallback)

Supported Syntax

SymPy Examples

diff(x**2, x) — Differentiate x² with respect to x
integrate(sin(x), x) — Indefinite integral of sin(x)
solve(x**2 - 4, x) — Solve x² - 4 = 0
limit(sin(x)/x, x, 0) — Evaluate limit as x approaches 0
simplify((x**2 - 1)/(x - 1)) — Simplify expression
expand((x + 1)**3) — Expand polynomial
factor(x**2 - 4) — Factor polynomial

NumPy Examples

np.mean([1, 2, 3, 4, 5]) — Calculate mean
np.std([1, 2, 3]) — Standard deviation
np.dot([1, 2], [3, 4]) — Dot product
np.linalg.det(array([[1, 2], [3, 4]])) — Matrix determinant
np.linspace(0, 10, 5) — Generate evenly spaced values

SciPy Examples

stats.norm.cdf(0) — Standard normal CDF at 0
stats.norm.pdf(0, loc=0, scale=1) — Normal PDF
special.gamma(5) — Gamma function
stats.t.ppf(0.975, df=10) — t-distribution critical value

Unicode Support

The following Unicode symbols are automatically converted:

π → pi, ∞ → oo, √ → sqrt, ∂ → diff, ∫ → integrate
^ is converted to ** for exponentiation

Security

Expressions are sandboxed. The following are blocked:

import, exec, eval, compile, open statements
Access to os, sys, subprocess, pathlib, shutil
Dunder attributes (__)
Semicolons and newlines (no multi-statement expressions)
Only whitelisted functions and namespaces are permitted

Response Fields

expression — The original expression submitted
engine_used — Which engine processed the expression (sympy, numpy, or scipy)
execution_time_seconds — How long the computation took
result — The computed result (JSON-serializable)
result_str — String representation of the result
metadata — Additional info (result_type, variables if symbolic)

When To Use

Use this skill for Complex Mathematics Engine on AgentPMT.
Use it when an agent needs this specific tool's behavior, schema, inputs, outputs, and invocation shape.
Search and activation keywords: complex mathematics engine, calculus, solve derivative, calculate integral, find limit of function, calculate, expression, engine hint.
Supported action names: calculate.

Use Cases

Calculus
Solve Derivative
Calculate Integral
Find Limit of Function
Algebra
Solve Equation for Variable
Simplify Polynomial
Factor Expression
Expand Formula
Linear Algebra
Matrix Multiplication
Invert Matrix
Solve Linear System
Calculate Determinant
Find Eigenvalues
Statistics

Categories And Industries

No categories or industry tags are published for this tool.

Actions And Schema

Complete generated action schema: ./schema.md. Supported action count: 1. x402 availability: not enabled for this product.

calculate (action slug: calculate): Evaluate a mathematical expression using SymPy (symbolic), NumPy (numerical/arrays), or SciPy (statistics/optimization). Supports calculus, linear algebra, statistics, and more. Price: 5 credits. Parameters: engine_hint, expression.

Live Schema And Examples

Use the compact schema above for ordinary calls. Before a new production integration, or whenever parameters, enum values, nested objects, outputs, or examples are unclear, fetch live details first.

Exact schema: call agentpmt-tool-search-and-execution with action: "get_schema", and tool_id: "complex-mathematics-engine".
Detailed examples: call agentpmt-tool-search-and-execution with action: "get_instructions" and tool_id: "complex-mathematics-engine", or call this product with action: "get_instructions" when the product tool is already selected.
Treat returned live schema and instructions as more specific than this generated summary.

MCP schema lookup through the main AgentPMT MCP server:

json

{
  "method": "tools/call",
  "params": {
    "name": "AgentPMT-Tool-Search-and-Execution",
    "arguments": {
      "action": "get_schema",
      "tool_id": "complex-mathematics-engine"
    }
  }
}

For live examples, keep the same MCP tool and use these arguments:

json

{
  "action": "get_instructions",
  "tool_id": "complex-mathematics-engine"
}

Authenticated AgentPMT REST schema lookup body:

json

{
  "name": "agentpmt-tool-search-and-execution",
  "parameters": {
    "action": "get_schema",
    "tool_id": "complex-mathematics-engine"
  }
}

Authenticated AgentPMT REST live examples body:

json

{
  "name": "agentpmt-tool-search-and-execution",
  "parameters": {
    "action": "get_instructions",
    "tool_id": "complex-mathematics-engine"
  }
}

Call This Tool

Product slug: complex-mathematics-engine

Marketplace page: https://www.agentpmt.com/marketplace/complex-mathematics-engine

AgentPMT account route: first use ../agentpmt-account-mcp-rest-api-setup to connect the main MCP server or REST API for an Agent Group where this tool is enabled.
x402 route: not enabled for this product.
AgentPMT overview: use ../what-is-agentpmt for marketplace, Agent Group, workflow, MCP, REST, and payment concepts.

If those setup skills are not installed beside this product skill, use the downloads below.

Core AgentPMT setup skills:

What AgentPMT is: ../what-is-agentpmt
- ClawHub page: https://clawhub.ai/agentpmt/what-is-agentpmt
- OpenClaw install: openclaw skills install what-is-agentpmt
- skills.sh install: npx skills add AgentPMT/agent-skills --skill what-is-agentpmt
AgentPMT account MCP/REST setup: ../agentpmt-account-mcp-rest-api-setup
- ClawHub page: https://clawhub.ai/agentpmt/agentpmt-account-mcp-rest-api-setup
- OpenClaw install: openclaw skills install agentpmt-account-mcp-rest-api-setup
- skills.sh install: npx skills add AgentPMT/agent-skills --skill agentpmt-account-mcp-rest-api-setup

skills.sh install script:

bash

npx skills add AgentPMT/agent-skills --skill what-is-agentpmt
npx skills add AgentPMT/agent-skills --skill agentpmt-account-mcp-rest-api-setup

MCP call shape after the main AgentPMT MCP server is connected:

json

{
  "method": "tools/call",
  "params": {
    "name": "Complex-Mathematics-Engine",
    "arguments": {
      "action": "calculate",
      "engine_hint": "auto",
      "expression": "example expression"
    }
  }
}

Use the exact tool name returned by tools/list; the name above is the expected readable form.

Authenticated AgentPMT REST call body:

json

{
  "name": "complex-mathematics-engine",
  "parameters": {
    "action": "calculate",
    "engine_hint": "auto",
    "expression": "example expression"
  }
}

Use the setup skill for the account connection details before making REST calls.

Response Handling

Treat the returned JSON as the source of truth for this tool call.
If the response includes warnings or correction targets, apply them before retrying.
If the response includes a passed or success-style boolean, use it as the workflow gate.
If validation fails or the response shape is unclear, call get_schema or get_instructions before retrying.
If calculate fails, preserve the request parameters and retry only after fixing schema, auth, or payment errors.

Security

Do not place account secrets, wallet private keys, mnemonics, signatures, or payment headers in prompts or logs.
Keep tool inputs scoped to the minimum content needed for the task.
Use the setup skills for credential handling; this product skill only defines product-specific behavior.

AgentPMT Reference

What AgentPMT is: ../what-is-agentpmt (ClawHub: what-is-agentpmt, page: https://clawhub.ai/agentpmt/what-is-agentpmt; skills.sh: npx skills add AgentPMT/agent-skills --skill what-is-agentpmt)
AgentPMT account MCP/REST setup: ../agentpmt-account-mcp-rest-api-setup (ClawHub: agentpmt-account-mcp-rest-api-setup, page: https://clawhub.ai/agentpmt/agentpmt-account-mcp-rest-api-setup; skills.sh: npx skills add AgentPMT/agent-skills --skill agentpmt-account-mcp-rest-api-setup)
Marketplace product: https://www.agentpmt.com/marketplace/complex-mathematics-engine
AgentPMT main MCP server: https://api.agentpmt.com/mcp/
AgentPMT REST invoke endpoint: https://api.agentpmt.com/products/purchase

Complex Mathematics Engine

Install

Complex Mathematics Engine

Freshness

What This Tool Does

Product Instructions

Complex Mathematics Engine (009) - Instructions

Overview

Action: calculate

Parameters

Engine Auto-Detection

Supported Syntax

SymPy Examples

NumPy Examples

SciPy Examples

Unicode Support

Security

Response Fields

When To Use

Use Cases

Categories And Industries

Actions And Schema

Live Schema And Examples

Call This Tool

Response Handling

Security

AgentPMT Reference

Action: `calculate`