open-index/open-npm huggingface dataset:
npm package: @iflow-mcp/garc33_js-sandbox-mcp-server
Description: A Model Context Protocol server
Latest version: 0.2.0

[![MseeP.ai Security Assessment Badge](https://mseep.net/pr/garc33-js-sandbox-mcp-server-badge.png)](https://mseep.ai/app/garc33-js-sandbox-mcp-server)

# js-sandbox MCP Server

[![smithery badge](https://smithery.ai/badge/@garc33/js-sandbox-mcp-server)](https://smithery.ai/server/@garc33/js-sandbox-mcp-server)

A Model Context Protocol server that provides a secure JavaScript execution environment.

<a href="https://glama.ai/mcp/servers/agatnhlgki">
  <img width="380" height="200" src="https://glama.ai/mcp/servers/agatnhlgki/badge" alt="JavaScript Sandbox Server MCP server" />
</a>

## Features

### Tools
- `execute_js` - Executes JavaScript code in an isolated environment
  - Parameters:
    - `code` (required): JavaScript code to execute
    - `timeout` (optional): Maximum execution time in milliseconds (100-30000ms)
    - `memory` (optional): Memory limit in bytes (1MB-100MB)
  - Returns the result of code execution

### Security
- Isolated code execution in a controlled environment
- Configurable execution time and memory limits
- Protection against malicious code

## Development

Install dependencies:
```bash
npm install
```

Build the server:
```bash
npm run build
```

Development mode with auto-rebuild:
```bash
npm run watch
```

## Installation

### Installing via Smithery

To install JavaScript Sandbox Server for Claude Desktop automatically via [Smithery](https://smithery.ai/server/@garc33/js-sandbox-mcp-server):

```bash
npx -y @smithery/cli install @garc33/js-sandbox-mcp-server --client claude
```

To use with Claude Desktop, add the server configuration:

MacOS: `~/Library/Application Support/Claude/claude_desktop_config.json`
Windows: `%APPDATA%/Claude/claude_desktop_config.json`

```json
{
  "mcpServers": {
    "js-sandbox": {
      "command": "/path/to/js-sandbox/build/index.js"
    }
  }
}
```

### Debugging

Since MCP servers communicate over stdio, debugging can be challenging. We recommend using [MCP Inspector](https://github.com/modelcontextprotocol/inspector), which is available as:

```bash
npm run inspector
```

The Inspector will provide a URL to access debugging tools in your browser.
npm package: @iflow-mcp/garoth-wolframalpha-mcp-server
Description: Uses WolframAlpha to answer math and other queries
Latest version: 1.0.1

# WolframAlpha LLM MCP Server

<img src="assets/wolfram-llm-logo.png" width="256" alt="WolframAlpha LLM MCP Logo" />

A Model Context Protocol (MCP) server that provides access to WolframAlpha's LLM API. https://products.wolframalpha.com/llm-api/documentation

<div>
  <img src="assets/readme-screen-1.png" width="609" alt="WolframAlpha MCP Server Example 1" /><br/><br/>
  <img src="assets/readme-screen-2.png" width="609" alt="WolframAlpha MCP Server Example 2" />
</div>

## Features

- Query WolframAlpha's LLM API with natural language questions
- Answer complicated mathematical questions
- Query facts about science, physics, history, geography, and more
- Get structured responses optimized for LLM consumption
- Support for simplified answers and detailed responses with sections

## Available Tools

- `ask_llm`: Ask WolframAlpha a question and get a structured llm-friendly response
- `get_simple_answer`: Get a simplified answer
- `validate_key`: Validate the WolframAlpha API key

## Installation

```bash
git clone https://github.com/Garoth/wolframalpha-llm-mcp.git
npm install
```

## Configuration

1. Get your WolframAlpha API key from [developer.wolframalpha.com](https://developer.wolframalpha.com/)

2. Add it to your Cline MCP settings file inside VSCode's settings (ex. ~/.config/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json):

```json
{
  "mcpServers": {
    "wolframalpha": {
      "command": "node",
      "args": ["/path/to/wolframalpha-mcp-server/build/index.js"],
      "env": {
        "WOLFRAM_LLM_APP_ID": "your-api-key-here"
      },
      "disabled": false,
      "autoApprove": [
        "ask_llm",
        "get_simple_answer",
        "validate_key"
      ]
    }
  }
}
```

## Development

### Setting Up Tests

The tests use real API calls to ensure accurate responses. To run the tests:

1. Copy the example environment file:
   ```bash
   cp .env.example .env
   ```

2. Edit `.env` and add your WolframAlpha API key:
   ```
   WOLFRAM_LLM_APP_ID=your-api-key-here
   ```
   Note: The `.env` file is gitignored to prevent committing sensitive information.

3. Run the tests:
   ```bash
   npm test
   ```

### Building

```bash
npm run build
```

## License

MIT
npm package: @iflow-mcp/garystafford-mcp-server-node
Description: Two Node.js servers to demonstrate Model Context Protocol (MCP)
Latest version: 1.0.0

# AWS Strands Agents: Building and Connecting Your First Model Context Protocol (MCP) Server

## Overview

**Deploy an MCP server that searches for stock photography based on your current location and conditions using third-party APIs, and orchestrate it with Strands Agents and the Amazon Q Developer CLI.**

The Model Context Protocol (MCP) provides a standardized interface that enables AI agents to interact seamlessly with external services. In this post, we’ll demonstrate how to build an MCP server that integrates with Shutterstock — my favorite platform for high-quality licensed images, videos, music, and creative tools — using their robust API. We’ll then show how to expose these rich media search capabilities to agents developed with Strands Agents, AWS’s code-first framework for building production-ready AI agents. By orchestrating multiple agents, we’ll enable them to access the National Weather Service (NWS) API, intelligently select contextually relevant photos based on current weather conditions, and deliver results that showcase the power of reasoning and context-aware automation.

JavaScript/Node.js MCP Servers based on: [https://github.com/lucianoayres/mcp-server-node](https://github.com/lucianoayres/mcp-server-node).

## Prerequisites

* Git distributed version control system
* Node.js v22.16.01 LTS or newer (MCP servers)
* npm, Yarn, or other Node.js package manager
* Python 3.13.x (Strands Agents)
* pip, Poetry, or another Python package manager
* AWS Account (for access to Amazon Bedrock generative AI models; other model providers are also supported)
* Free Shutterstock Test Account and Token (Register at Shutterstock Developers)
* Optional: Amazon Q Developer CLI for last section of post

## Installation

1. **Clone Repository and Install Packages**

```bash
npm install -g corepack # if using yarn

git clone https://github.com/garystafford/aws-strands-agents-mcp-demo.git
cd aws-strands-agents-mcp-demo

yarn install
```

2. **Start MCP Servers**

```bash
# terminal window 1: STDIO transport
# environment variables are set by MCP server with STDIO
export API_KEY=abc-1234567890
export SHUTTERSTOCK_API_TOKEN=<YOUR_SHUTTERSTOCK_API_TOKEN>
node mcp-server.js

# terminal window 2: Streamable HTTP transport
# environmental variables are set by agent with Streamable HTTP
node mcp-server-remote.js
```

3. **Install Strands Agents**

```bash
python -m pip install virtualenv -Uqqq
python -m venv .venv
source .venv/bin/activate

python -m pip install pip -Uqqq
python -m pip install -r requirements.txt -Uqqq
```

4. **Start Strands Agents**

```bash
export API_KEY=abc-1234567890
export SHUTTERSTOCK_API_TOKEN=<YOUR_SHUTTERSTOCK_API_TOKEN>

python agent_stdio_multi_agent.py
```

---

_The contents of this repository represent my viewpoints and not of my past or current employers, including Amazon Web Services (AWS). All third-party libraries, modules, plugins, and SDKs are the property of their respective owners._