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
- 01Convert text and emoji to vectors
- 02Calculate vector cosine similarity
- 03Measure Euclidean and Manhattan distances
- 04Compute helical components
- 05Parse Emojikey V3 strings
Use cases
- 01Compare the semantic similarity of emoji
- 02Measure distances between vectors or dimensions
- 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.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMESemantic 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
-arm64flag ensures native Apple Silicon execution - Make sure
/Users/rob/.local/bin/uvexists (install withcurl -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 embeddingsemantic_calc_emoji_to_vector: Convert emoji to a vector embeddingsemantic_calc_cosine_similarity: Calculate cosine similarity between vectorssemantic_calc_euclidean_distance: Calculate Euclidean distance between vectorssemantic_calc_manhattan_distance: Calculate Manhattan distance between vectorssemantic_calc_dimension_distance: Calculate similarity between dimensionssemantic_calc_calculate_helical_components: Calculate helical components from magnitude/phasesemantic_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
Comments
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