Semantic Calculator MCP

Overview

The Semantic Calculator MCP is a lightweight, Python‑based server that equips AI assistants with advanced semantic analytics. It turns raw text and emoji into high‑dimensional vector embeddings, computes similarity metrics, extracts phase‑angle representations via helical components, and parses the Emojikey V3 format used in many emoji‑centric applications. By exposing these capabilities as MCP tools, developers can integrate nuanced semantic reasoning directly into Claude or other AI workflows without leaving the chat interface.

This server solves a common pain point for developers building conversational agents that need to understand meaning beyond surface syntax. Traditional NLP pipelines often require heavy infrastructure or complex model deployment steps. The Semantic Calculator abstracts that complexity, delivering ready‑to‑use vector operations—cosine similarity, Euclidean and Manhattan distances, dimension‑specific distance metrics—all through simple MCP calls. It also supports converting emoji to vectors and parsing Emojikey strings, enabling AI assistants to interpret and manipulate emoji in a mathematically rigorous way.

Key features include:

• Vector conversion for plain text and emoji, leveraging Sentence‑BERT embeddings.
• Similarity and distance calculations (cosine, Euclidean, Manhattan) that can be invoked from any MCP‑enabled client.
• Helical component extraction for phase‑angle analysis, useful in signal processing or temporal modeling.
• Emojikey V3 parsing to interpret complex emoji sequences and retrieve their semantic components.
• Dimension‑distance analysis that compares specific vector dimensions, aiding feature‑level insights.

Real‑world scenarios span from building empathetic chatbots that gauge emotional similarity between user inputs and pre‑defined responses, to data‑driven emoji recommendation engines that suggest contextually appropriate icons. In a content moderation pipeline, the server can quickly compute semantic distances between user posts and policy‑violation templates. For creative applications, designers can use the emoji‑to‑vector conversion to generate mood boards or art prompts that align semantically with textual descriptions.

Integration is straightforward: once the MCP server is registered in Claude Desktop, each core function becomes an available tool. Developers can invoke them with JSON payloads, chaining results to build complex reasoning chains—e.g., converting a user’s text to a vector, comparing it with stored emoji vectors, and returning the most semantically aligned emoji. The server’s lightweight dependencies (Python 3.10+, sentence‑transformers, numpy) ensure it runs efficiently on Apple Silicon or Intel Macs, making it a practical addition to any developer’s AI toolkit.