Frequently Asked Questions (FAQ) about the Twitter Market Sentiment MCP Server

Q: What is the Twitter Market Sentiment MCP Server?

A: It’s a tool that analyzes real-time Twitter data to gauge market sentiment for stocks and financial markets, providing insights into trends and price mentions via the Model Context Protocol (MCP).

Q: What is MCP?

A: MCP stands for Model Context Protocol. It is an open protocol that standardizes how applications provide context to LLMs, enabling seamless integration with AI models.

Q: What type of data does this server analyze?

A: The server analyzes tweets related to stocks, financial markets, and related topics.

Q: What kind of insights can I get from this server?

A: You can get insights such as sentiment scores for specific stocks, bullish/bearish classifications, price mention analysis, identification of market trends, and real-time market monitoring.

Q: What are the prerequisites for setting up this server?

A: You need Python 3.9 or higher, a Twitter Developer Account with API v2 credentials, and either pip or uv package manager.

Q: How do I get Twitter API v2 credentials?

A: You need to create a Twitter Developer Account at https://developer.twitter.com, create a project and app, enable OAuth 2.0, and generate the required API keys and tokens.

Q: How do I install the server?

A: First, clone the repository, then create and activate a virtual environment, and finally, install the dependencies using pip install -r requirements.txt.

Q: How do I run the server?

A: After setting up the environment variables, start the server with the command: uvicorn src.twitter_mcp:app --reload.

Q: Where can I access the API documentation?

A: The API documentation is available at http://localhost:8000/docs or http://localhost:8000/redoc.

Q: How can I test my setup?

A: Run the test environment script using the command: python src/test_env.py.

Q: What is Smithery Deployment?

A: Smithery deployment is a streamlined method to deploy the server with pre-configured settings for HTTP transport, environment variable management, CORS support, JSON logging, and a health check endpoint.

Q: What are the key deployment features on Smithery?

A: Key features include HTTP transport, environment variable management, CORS support, JSON logging, and a health check endpoint.

Q: How can I contribute to this project?

A: Fork the repository, create your feature branch, commit your changes, push to the branch, and open a pull request.

Q: What license is this project under?

A: This project is licensed under the MIT License.

Q: Can I use this server for commercial purposes?

A: Yes, you can use it for commercial purposes as long as you comply with the terms of the MIT License and Twitter API terms of service.

Q: How does the server handle rate limits from the Twitter API?

A: The server should implement error handling and retry mechanisms to manage rate limits gracefully. You may need to adjust your usage patterns to stay within the Twitter API rate limits.

Q: Does this server provide historical sentiment analysis?

A: The server can provide sentiment analysis for a specified lookback period (e.g., 24 hours). Historical sentiment analysis beyond that would require storing the data.

Q: How do I integrate this server with the UBOS platform?

A: The MCP Server integrates with the UBOS platform by allowing you to connect it to your existing AI agent workflows. You can create AI agents that monitor market sentiment and send you alerts based on the analysis.

Q: What is UBOS?

A: UBOS is a full-stack AI Agent Development Platform focused on bringing AI Agents to every business department. It helps orchestrate AI Agents, connect them with enterprise data, build custom AI Agents with LLM models, and create Multi-Agent Systems.