MCPlex-AI-v1.0

MCPlex-AI v1.0 is a command-line driven MCP server that enables a Google Gemini-based AI to orchestrate external tools and APIs. The server exposes a suite of tools (such as print-menu, news-by-topic, wikipedia-search, github-repo-info, movie-ratings, local-file-search, truth_table, define_word, and adder for basic testing) that the AI can invoke to fetch real-time information, perform calculations, search content, and interact with external services. Run the server locally, connect a CLI client, and instruct the AI to call these tools as part of a conversation. You can request the AI to summarize news, query Wikipedia, fetch GitHub repo details, post to X (Twitter) when configured, or search local files to satisfy user requests. The UI remains minimal—the AI handles tool invocation requests and returns the formatted results for display in the terminal.

Typical usage flows include: asking the AI to fetch the latest AI news using news-by-topic, having it search Wikipedia with wikipedia-search for a topic, or using local-file-search to locate a config file. The adder tool is available for quick numerical checks, and movie-ratings provides detailed film information with OMDB data when configured. Use print-menu to display available tools and their descriptions to guide your interactions.

Prerequisites:

• Node.js (v14+ recommended; v16+ for best compatibility)
• npm (comes with Node.js)
• Git (optional, for cloning the repository)

Installation steps:

1. Clone the repository (or download the source): git clone https://github.com/your-organization/ecolash-mcplex-ai-v1.0.git cd ecolash-mcplex-ai-v1.0

2. Install dependencies: npm install

3. Configure environment variables:

   • GEMINI_API_KEY: Google Gemini API key
   • OMDB_API_KEY: OMDB API key (for movie-ratings tool)
   • TWITTER_BEARER_TOKEN: Twitter/X API bearer token (for twitter-X-post tool)
   • GITHUB_TOKEN: GitHub access token (optional but recommended for rate limits)
   • Set any additional environment variables your deployment requires

4. Start the MCP server: node server/index.js

5. If you prefer using a process manager (recommended for production):

   • Create a process entry (e.g., PM2) running: node server/index.js
   • Ensure environment variables are exported in the process environment

Notes:

• The UI is CLI-based. Interactions are through the MCP client which communicates with the server to execute tools.
• Ensure network access for Gemini, Wikipedia, GitHub, OMDB, and Twitter APIs as needed.

Tips and common issues:

• Ensure all required API keys are set in the environment; missing keys will cause tool calls to fail or degrade functionality.
• The adder tool is useful for quick internal testing of the MCP pipeline; use it to confirm end-to-end tool invocation works.
• If you encounter rate limits from external services (GitHub, OMDB, News sources), consider adding short delays or caching results where appropriate.
• For production deployments, run the server behind a reverse proxy and enable secure environment management (e.g., Docker secrets, environment variable vaults).
• The system can be extended by adding more tools to server/tools.js; registries can be updated in the Tool System to expose new capabilities to Gemini.