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Calculator MCP Server

MCP Server

LLM‑powered calculator for quick arithmetic tasks

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Updated Jun 20, 2025

About

A Model Context Protocol server that exposes basic calculator operations (add, sub, mul, div, mod, sqrt) to language models. It allows LLMs to perform arithmetic calculations via a simple API or browser automation.

Capabilities

Resources
Access data sources
Tools
Execute functions
Prompts
Pre-built templates
Sampling
AI model interactions

Calculator MCP – A Browser‑Based Arithmetic Engine for AI Assistants

The Calculator MCP server bridges the gap between large language models (LLMs) and a fully functional calculator running in a headless browser. By exposing arithmetic operations as MCP tools, the server lets AI assistants perform precise calculations without hard‑coding math logic into the model itself. This is especially useful when an assistant needs to evaluate expressions that involve floating‑point precision, complex formulas, or external data sources that the model cannot compute internally.

Developers can integrate this server into any MCP‑compatible workflow, such as GitHub Copilot or Claude’s external tool ecosystem. Once the server is running, an LLM can invoke tools like , , , , , and by passing arguments in JSON. The server executes the requested operation in a browser context, ensuring consistent behavior across environments and leveraging the same engine that powers typical web calculators. The result is streamed back to the model via Server‑Sent Events (SSE) or any custom transport, enabling real‑time interaction.

Key capabilities include:

  • Modular arithmetic tools that map directly to common calculator functions.
  • SSE transport support, allowing lightweight, low‑latency communication without WebSocket overhead.
  • Headless browser execution with optional headed mode for debugging or environments lacking a display server.
  • Programmatic API for creating in‑memory transports, making the server suitable for unit tests or isolated environments.

Real‑world scenarios benefit from this setup: a code‑generation assistant can automatically compute intermediate values while drafting algorithms; an educational chatbot can demonstrate step‑by‑step calculations; and data‑analysis pipelines can outsource numeric evaluation to a reliable, sandboxed engine. By decoupling computation from the LLM, developers avoid hard‑coding math logic and gain a reusable, testable component that can be swapped or upgraded independently.

Overall, the Calculator MCP server offers a lightweight, browser‑based arithmetic engine that integrates seamlessly into AI toolchains. Its simplicity, coupled with robust transport options and a clear set of arithmetic tools, makes it an attractive choice for developers looking to add reliable calculation capabilities to their AI assistants.