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611711Dark

MCP Calculate Server

by 611711Dark
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

MCP-Berechnungsserver

Ein mathematischer Berechnungsdienst basierend auf dem MCP-Protokoll und der SymPy-Bibliothek, der leistungsstarke symbolische Rechenfähigkeiten bietet.

Sicherheit

Ab Version 0.1.1 analysiert der Server Ausdrücke durch einen eingeschränkten SymPy-only-Evaluator. Er führt keinen beliebigen Python-Code aus, und es wird nur eine kuratierte Menge an mathematischen Symbolen, Funktionen und Matrixmethoden unterstützt.

Diese Version fügt außerdem eine Validierung für zu große Ausdrücke und umfangreiche Ergebnisse hinzu, um das Risiko von Denial-of-Service-Angriffen durch rechenintensive symbolische Berechnungen zu verringern.

Related MCP server: mcp-sympy

Hauptmerkmale

  • Grundlegende Operationen: Addition, Subtraktion, Multiplikation, Division, Potenzierung

  • Algebraische Operationen: Ausdruckserweiterung, Faktorisierung, Vereinfachung

  • Analysis: Differenzierung, Integration (bestimmt/unbestimmt), Grenzwertberechnung

  • Gleichungslösung: Algebraische Gleichungen, Gleichungssysteme

  • Matrixoperationen: Matrixinversion, Berechnung von Eigenwerten/Eigenvektoren

  • Reihenentwicklung: Taylor-Reihenentwicklung

  • Spezielle Funktionen: Trigonometrische, logarithmische, Exponentialfunktionen

Anwendungsbeispiele

# Basic operations
"2 + 3*5" → 17

# Algebraic operations
"expand((x + 1)**2)" → x² + 2x + 1
"factor(x**2 - 2*x - 15)" → (x - 5)(x + 3)

# Calculus
"diff(sin(x), x)" → cos(x)
"integrate(exp(x), (x, 0, 1))" → E - 1
"integrate(exp(-x**2)*sin(x), (x, -oo, oo))" → 0
"limit(tan(x)/x, x, 0)" → 1

# Equation solving
"solve(x**2 - 4, x)" → [-2, 2]
"solve([x**2 + y**2 - 1, x + y - 1], [x, y])" → [(0, 1), (1, 0)]

# Matrix operations
"Matrix([[1, 2], [3, 4]]).inv()" → [[-2, 1], [3/2, -1/2]]
"Matrix([[1, 2, 3], [4, 5, 6]]).eigenvals()" → {9/2 - sqrt(33)/2: 1, 9/2 + sqrt(33)/2: 1}
"Sum(k, (k, 1, 10)).doit()" → 55
"series(cos(x), x, 0, 4)" → 1 - x²/2 + O(x⁴)

Installation

Installation über Smithery

Um den Calculate Server für Claude Desktop automatisch über Smithery zu installieren:

npx -y @smithery/cli install @611711Dark/mcp_sympy_calculate_server --client claude

Lokale Installation

  1. Repository klonen:

    git clone https://github.com/611711Dark/mcp_calculate_server.git
cd mcp_calculate_server

  2. Virtuelle Umgebung erstellen und Abhängigkeiten installieren:

    uv venv
source .venv/bin/activate
uv pip install -e .

  3. Konfiguration:

    "calculate_expression1": {
   "isActive": false,
   "command": "python",
   "args": [
     "server.py"
   ],
   "cwd": "/path/to/mcp_calculate_server"
 }

API-Nutzung

Rufen Sie das Tool calculate_expression über das MCP-Protokoll auf, indem Sie einen mathematischen Ausdruck als String übergeben. Der Parser akzeptiert eine eingeschränkte Menge an SymPy-Ausdrücken wie Arithmetik, expand, factor, simplify, diff, integrate, limit, series, solve, Matrix(...).det()/inv()/eigenvals()/eigenvects() und Sum(...).doit().

Unterstützte Namen

  • Symbole: Kleinbuchstaben-Variablen wie x, y, z und k

  • Konstanten: pi, E, oo, I

  • Funktionen: Abs, sin, cos, tan, log, exp, sqrt, expand, factor, simplify, diff, integrate, limit, series, solve, Sum, Matrix

  • Matrixmethoden: .det(), .inv(), .eigenvals(), .eigenvects()

  • SymPy-Methode: .doit() auf unterstützten Objekten wie Sum(...)

Validierungsregeln

Ausdrücke, die auf beliebigen Python-Funktionen, Importen, Dateisystemzugriffen oder anderen nicht-mathematischen Konstrukten basieren, werden absichtlich abgelehnt. Sehr große Erweiterungen, hochkomplexe Berechnungen und überdimensionierte Ergebnisse können ebenfalls abgelehnt werden, um das Denial-of-Service-Risiko zu verringern. Schlüsselwortargumente, private Attribute, nicht unterstützte Matrixmethoden, fehlerhafte Matrizen und nicht unterstützte Namen werden mit einer Fehlermeldung abgelehnt.

Abhängigkeiten

  • mcp>=1.5.0

  • sympy>=1.13.3

Danksagungen

Danke an diesen Blog-Beitrag für die Einführung und an Stefano für seine Hilfe und verantwortungsvolle Offenlegung.

Lizenz

Dieses Projekt ist unter der MIT-Lizenz lizenziert. Siehe die Datei LICENSE.

中文版本

Available Tools

1 tool
calculate_expressionA

calculate mathematical expressions using the sympify function from sympy, parse and compute the input mathematical expression string, supports direct calls to SymPy functions (automatically recognizes x, y, z as symbolic variables) Parameters: expression (str): Mathematical expression, e.g., "223 - 344 * 6" or "sin(pi/2) + log(10)".Replace special symbols with approximate values, e.g., pi → 3.1415" Example expressions: "2 + 3*5" # Basic arithmetic → 17 "expand((x + 1)2)" # Expand → x² + 2x + 1 "diff(sin(x), x)" # Derivative → cos(x) "integrate(exp(x), (x, 0, 1))" # Definite integral → E - 1 "solve(x2 - 4, x)" # Solve equation → [-2, 2] "limit(tan(x)/x, x, 0)" # Limit → 1 "Sum(k, (k, 1, 10)).doit()" # Summation → 55 "Matrix([[1, 2], [3, 4]]).inv()" # Matrix inverse → [[-2, 1], [3/2, -1/2]] "simplify((x2 - 1)/(x + 1))" # Simplify → x - 1 "factor(x2 - 2*x - 15)" # Factorize → (x - 5)(x + 3) "series(cos(x), x, 0, 4)" # Taylor series → 1 - x²/2 + x⁴/24 + O(x⁴) "integrate(exp(-x*2)*sin(x), (x, -oo, oo))" # Complex integral "solve([x**2 + y*2 - 1, x + y - 1], [x, y])" # Solve system of equations "Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]]).eigenvals()" # Matrix eigenvalues Returns: str: Calculation result. If the expression cannot be parsed or computed, returns an error message (str).

ParametersJSON Schema
NameRequiredDescriptionDefault
expressionYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: it uses sympify from sympy, supports symbolic variables (x, y, z), handles special symbols (e.g., pi → 3.1415), and returns a string result or error message. It also lists many example behaviors (e.g., derivatives, integrals). However, it doesn't mention potential limitations like performance, complexity bounds, or specific error conditions beyond 'cannot be parsed or computed.'

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose and parameter explanation, but it includes a lengthy list of 14 example expressions. While these examples are informative, they make the description verbose and could be trimmed or summarized. The structure is logical but not optimally concise, as some examples might be redundant for conveying the tool's capabilities.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (mathematical computation with sympy), the description is highly complete. It explains the purpose, parameter semantics in detail, behavioral traits, and includes an output schema (returns str or error). With no annotations, it covers all necessary aspects: how to use it, what it does, and what to expect, making it sufficient for an AI agent to invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 0%, so the description must fully compensate. It adds rich semantics: it defines the 'expression' parameter as a 'Mathematical expression' with examples (e.g., '2 + 3*5'), explains special symbol handling (pi → 3.1415), and provides numerous detailed examples showing syntax and usage. This goes far beyond the basic schema, making the parameter's meaning and format clear.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'calculate mathematical expressions using the `sympify` function from `sympy`, parse and compute the input mathematical expression string.' It specifies the exact method (sympify from sympy) and scope (mathematical expressions), making it highly specific. With no sibling tools, differentiation isn't needed, but the description is precise about what it does.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage through extensive examples (e.g., 'Example expressions:') that show various mathematical operations, suggesting when to use it for different types of calculations. However, it lacks explicit guidance on when not to use it or alternatives, and there are no sibling tools to compare against. The examples serve as implicit guidance but aren't structured as explicit rules.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.1/5.0
Disambiguation5/5

With only one tool, there is no possibility of ambiguity or confusion between tools. The single tool 'calculate_expression' has a clearly defined purpose that cannot be mistaken for any other tool in this server.

Naming Consistency5/5

The single tool name 'calculate_expression' follows a clear verb_noun pattern. With only one tool, naming consistency is inherently perfect as there are no other tools to compare against or create inconsistencies with.

Tool Count2/5

A single tool server is generally too minimal for most practical purposes, even for a focused domain like mathematical calculation. While the tool is powerful, having only one tool feels thin and limiting for what appears to be a comprehensive mathematical computation server.

Completeness3/5

The single tool covers a wide range of mathematical operations through expression parsing, but there are notable gaps in the surface area. For a calculation server, one might expect separate tools for different mathematical domains (algebra, calculus, matrix operations) or at least tools for common specific operations beyond general expression evaluation.

Maintenance

ActivityInactive
ResponsivenessResponsive

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