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SSE Calculator MCP Server

SSE Calculator MCP Server

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github.com/phamtienducengScience & Education0Updated 77w ago
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About SSE Calculator MCP Server

SSE Calculator MCP Server is a stateful calculator service for AI assistants, with a web interface and calculation history. It uses Server-Sent Events to maintain calculator sessions and stream communication.

Key features

  1. 01Calculator operations over SSE
  2. 02Stateful calculation sessions
  3. 03Calculation history tracking
  4. 04Web-based calculator interface

Use cases

  1. 01Perform calculations in an ongoing session
  2. 02Review a session's earlier calculations
  3. 03Use a browser-based calculator interface

MCP support

The server implements MCP for AI assistant integration and can be configured in MCP settings using its calculator stream endpoint.

#mathgpt#math-solver#math-assistant

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

SSE Calculator MCP Server

A stateful calculator server that uses Server-Sent Events (SSE) for real-time communication. This server implements the Model Context Protocol (MCP) for integration with AI assistants.

Features

  • Real-time calculator operations using SSE
  • Stateful sessions for ongoing calculations
  • History tracking for all calculations
  • Web-based calculator interface
  • MCP integration for AI assistants

Architecture

The server is built using NestJS and implements the following components:

  • Session Service: Manages calculator sessions and state
  • Calculator Service: Performs calculation operations
  • SSE Controller: Handles client connections and event streaming

Getting Started

Prerequisites
  • Node.js (v14 or higher)
  • npm (v6 or higher)
Installation
  1. Clone the repository
  2. Install dependencies:
npm install
  1. Start the server:
npm run start:dev
  1. Open the calculator interface in your browser:
http://localhost:3000

API Endpoints

SSE Stream
GET /calculator/stream

Establishes an SSE connection and creates a new calculator session.

Calculate
POST /calculator/calculate/:sessionId

Performs a calculation operation on the specified session.

Request body:

{
  "operation": "add", // add, subtract, multiply, divide, clear
  "a": 5,
  "b": 3
}
History
GET /calculator/history/:sessionId

Retrieves the calculation history for the specified session.

Sessions
GET /calculator/sessions

Retrieves all active calculator sessions.

MCP Integration

To use this server as an MCP server, add the following configuration to your MCP settings file:

{
  "mcpServers": {
    "remote-calculator": {
      "url": "http://localhost:3000/calculator/stream",
      "disabled": false,
      "alwaysAllow": [],
      "timeout": 60
    }
  }
}

Deployment Options

For production deployment, you can use the following options:

  1. Docker: A Dockerfile is provided for containerized deployment

    • See Dockerfile and docker-compose.yml for configuration

  2. Smithery.ai: Deploy directly from GitHub

    • See SMITHERY_DEPLOYMENT.md for detailed instructions
    • Configuration provided in smithery.json

  3. Heroku/DigitalOcean: Cloud platform deployment

    • See DEPLOYMENT.md for step-by-step instructions

  4. Self-hosted: Use NGINX or similar for SSL termination and load balancing

Hosting Your Own MCP Server

After deploying your server, update your MCP client configuration to point to your hosted URL:

{
  "mcpServers": {
    "remote-calculator": {
      "url": "https://your-deployed-url.com/calculator/stream",
      "disabled": false,
      "alwaysAllow": [],
      "timeout": 60
    }
  }
}

License

MIT

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.