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611711Dark

MCP Calculate Server

by 611711Dark
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Python
Hybrid

MCP 計算サーバー

MCPプロトコルとSymPyライブラリに基づいた数学計算サービスであり、強力な記号計算機能を提供します。

セキュリティ

バージョン 0.1.1 時点で、このサーバーは制限されたSymPy専用の評価器を通じて式を解析します。任意のPythonコードを実行することはなく、厳選された数学記号、関数、および行列メソッドのみがサポートされています。

また、このリリースでは、高コストな記号計算によるサービス拒否（DoS）リスクを軽減するため、過大な式や巨大な結果に対する検証機能が追加されました。

Related MCP server: mcp-sympy

主な機能

  • 基本演算: 加算、減算、乗算、除算、べき乗

  • 代数演算: 式の展開、因数分解、簡略化

  • 微積分: 微分、積分（定積分/不定積分）、極限計算

  • 方程式の解法: 代数方程式、連立方程式

  • 行列演算: 行列の逆行列、固有値/固有ベクトルの計算

  • 級数展開: テイラー級数展開

  • 特殊関数: 三角関数、対数関数、指数関数

使用例

# 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)

インストール

Smithery経由でのインストール

Smithery を使用してClaude Desktop用のCalculate Serverを自動的にインストールするには：

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

ローカルインストール

  1. リポジトリをクローンします：

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

  2. 仮想環境を作成し、依存関係をインストールします：

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

  3. 設定：

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

APIの使用方法

数学的な式文字列を渡すことで、MCPプロトコル経由で calculate_expression ツールを呼び出します。パーサーは、算術演算、expand、factor、simplify、diff、integrate、limit、series、solve、Matrix(...).det()/inv()/eigenvals()/eigenvects()、および Sum(...).doit() といった制限されたSymPy式のセットを受け入れます。

サポートされている名前

  • 記号: x、y、z、k などの小文字の変数

  • 定数: pi、E、oo、I

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

  • 行列メソッド: .det()、.inv()、.eigenvals()、.eigenvects()

  • SymPyメソッド: Sum(...) などのサポート対象オブジェクトに対する .doit()

検証ルール

任意のPython機能、インポート、ファイルシステムアクセス、またはその他の数学的でない構成要素に依存する式は、意図的に拒否されます。 非常に大きな展開、複雑度の高い解法、および過大な結果も、サービス拒否リスクを軽減するために拒否される場合があります。 キーワード引数、プライベート属性、サポートされていない行列メソッド、不正な形式の行列、およびサポートされていない名前は、エラーメッセージとともに拒否されます。

依存関係

  • mcp>=1.5.0

  • sympy>=1.13.3

謝辞

導入にあたり こちらのブログ記事 に感謝します。また、Stefano 氏の協力と責任ある開示に感謝します。

ライセンス

このプロジェクトはMITライセンスの下でライセンスされています。LICENSE ファイルを参照してください。

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.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool update
    • First observedcalculate_expression
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

A4.1/5.0

Scored across 1 tool

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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