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Semantic Calculator MCP

Semantic Calculator MCP

Local
github.com/identimojiMemory & Knowledge1Updated 75w ago

About Semantic Calculator MCP

Semantic Calculator MCP is a Python MCP server for semantic calculations on vectors, text, and emoji, including Emojikey V3 parsing. It offers embedding conversion and several distance and similarity operations.

Key features

  1. 01Convert text and emoji to vectors
  2. 02Calculate vector cosine similarity
  3. 03Measure Euclidean and Manhattan distances
  4. 04Compute helical components
  5. 05Parse Emojikey V3 strings

Use cases

  1. 01Compare the semantic similarity of emoji
  2. 02Measure distances between vectors or dimensions
  3. 03Analyze Emojikey V3 strings

MCP support

It exposes semantic calculation functions as MCP tools, including text and emoji vector conversion, similarity and distance calculations, and Emojikey parsing.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Semantic Calculator MCP

A Python-based MCP tool for semantic operations on vectors, text, and emoji, with specialized support for the Emojikey V3 system.

Features

  • Calculate semantic similarities between vectors
  • Convert text and emoji to vector embeddings
  • Calculate helical components for phase angle representations
  • Parse and analyze Emojikey V3 strings
  • Calculate semantic field distance between dimensions

Requirements

  • Python 3.10+ (required by MCP SDK)
  • Apple Silicon Mac (M1/M2/M3) or Intel Mac
  • Claude Desktop application

Installation

1. Install Dependencies
# Clone the repository
git clone https://github.com/yourusername/mcp-server-semantic-calc.git
cd mcp-server-semantic-calc

# Install dependencies using uv (recommended)
uv sync

# Or install in development mode
uv pip install -e .
2. Configure Claude Desktop

Add this configuration to your Claude Desktop settings (typically ~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "Semantic Calculator": {
      "command": "arch",
      "args": [
        "-arm64",
        "/Users/rob/.local/bin/uv",
        "--directory",
        "/Users/rob/repos/mcp-server-semantic-calc",
        "run",
        "-m",
        "semantic_calculator",
        "mcp"
      ]
    }
  }
}

Important Notes:

  • Replace /Users/rob/ with your actual home directory path
  • The -arm64 flag ensures native Apple Silicon execution
  • Make sure /Users/rob/.local/bin/uv exists (install with curl -LsSf https://astral.sh/uv/install.sh | sh)
3. Restart Claude Desktop

Restart Claude Desktop to load the new MCP server.

Usage

Direct Python Usage
from semantic_calculator.core import SemanticCalculator

# Initialize the calculator
calc = SemanticCalculator()

# Calculate similarity between two emoji
similarity = calc.semantic_calculator_cosine_similarity(
    calc.semantic_calculator_emoji_to_vector("🧠"),
    calc.semantic_calculator_emoji_to_vector("🎨")
)
print(f"Similarity: {similarity}")
MCP Usage (in Claude)

Once configured in Claude Desktop, you can use it with:

// Convert emoji to vector
const brainVector = semantic_calc_emoji_to_vector({
  emoji: "🧠"
});

const artVector = semantic_calc_emoji_to_vector({
  emoji: "🎨"
});

// Calculate similarity
const similarity = semantic_calc_cosine_similarity({
  vector1: brainVector,
  vector2: artVector
});

console.log(`Similarity: ${similarity}`);

Note: All tool functions have been prefixed with semantic_calc_ to avoid naming conflicts with other tools.

Example Scripts
# After installation, run examples
python -m semantic_calculator.examples.vector_operations
python -m semantic_calculator.examples.calculate_emoji_similarity
python -m semantic_calculator.examples.analyze_emojikey

Core Functions

  • semantic_calc_text_to_vector: Convert text to a vector embedding
  • semantic_calc_emoji_to_vector: Convert emoji to a vector embedding
  • semantic_calc_cosine_similarity: Calculate cosine similarity between vectors
  • semantic_calc_euclidean_distance: Calculate Euclidean distance between vectors
  • semantic_calc_manhattan_distance: Calculate Manhattan distance between vectors
  • semantic_calc_dimension_distance: Calculate similarity between dimensions
  • semantic_calc_calculate_helical_components: Calculate helical components from magnitude/phase
  • semantic_calc_parse_emojikey_string: Parse emojikey strings

Dependencies

Core Dependencies (Required)

These dependencies are automatically installed via pyproject.toml:

  • Python 3.10+ - Required by MCP SDK
  • mcp>=0.9.0 - Model Context Protocol SDK
  • sentence-transformers>=2.2.0 - For semantic vector embeddings
  • numpy>=1.20.0 - For vector operations
  • scikit-learn>=1.0.0 - For distance calculations
  • torch>=1.10.0 - Required by SentenceBERT
Visualization Dependencies (Optional)
  • matplotlib>=3.4.0 - For basic visualization
  • plotly>=5.5.0 - For interactive 3D visualization
  • umap-learn>=0.5.2 - For dimensionality reduction
  • seaborn>=0.11.2 - For enhanced visualizations
Development Dependencies
  • pytest>=7.0.0 - For testing (install with uv sync --dev)

Note: The semantic calculator will refuse to start if any of the core dependencies are not installed. All dependencies are managed through pyproject.toml and installed automatically with uv sync.

License

MIT

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