MCP Server for X (formerly Twitter) – A Comprehensive Guide to Using MCP Servers with Existing Tools

The night of October 11, 2024, marked the historic crash of Cryptocurrency. The immediate aftermath of the crash resulted in many people losing their positions and others being able to profit from arbitrage opportunities. The difference between the two groups is that the successful group was using automated solutions with the correct technology and processes. In this post, we will discuss the use of MCP Servers on X (Twitter), a technology that allows for a clean and programmatically accessible way of getting information from Twitter without the need for additional tools. If you work with cryptocurrency communities, observe or report on Alpha via Twitter, or create Artificial Intelligence (AI) Agents, the solutions that MCP servers provide will offer a great deal of relief from many of the common issues that exist around social media automation.

In the last two years, we have attempted to develop 43 various automated work processes through social media networks. The conclusion is very clear: The processes of Web Scraping and Ready-Made APIs become obsolete at an extremely rapid pace. MCP Servers solve this problem at the Architectural Level by creating a standardized method for connecting Data to AI Agents. The time it takes to deploy an MCP Server ranges from multiple days to less than one hour. An MCP Server is not just another method of Automation for working with Twitter; it is a radical change in how you work with social media networks.

What is an MCP Server for X?

The MCP server for X is a local server based on the Model Context Protocol (MCP) that provides a standard way to Programmatically access Twitter Data. Rather than needing to have a separate Unique Script/Method for each specific use case, you simply install an MCP server and then attach/mount the connection points of MCP Server to your AI Agent or No-Code Platform. You will have access to a Ready-Made Set of Functions, such as Searching for Tweets, Publishing Content, Liking Tweets, Following Users, and Sending Direct Messages (DM's).

MCP is an Open Protocol Developed by Anthropic that enables AI Models to interact with External Data Sources in a Standardized Way. For X, there's a TypeScript wrapper around the Twitter API that allows you to interact with it through one cohesive interface rather than dealing with the Twitter API directly (i.e., reading its documentation; limiting your rates through the Twitter API rate limits; managing authentication crutches such as OAuth, etc.). You simply set up a server, provide the server your API keys, and interact with Twitter using standard MCP methods.

The architecture of X using the MCP (Model Context Protocol) consists of three main components:

• MCP Adapter to the Twitter API (handles authentication, limit tracking, and the formatting of requests)
• Set of MCP Methods (which are "ready to use" functions, including — but not limited to: search_tweets; post_tweet; like_tweet; get_user_info; and send_dm)
• Local Server (which can be run on your computer or in the cloud) that accepts requests from the AI Model, allowing you to run it in multiple locations

Some Advantages of Using MCP Server

Quick Time to Implementation — From your installation of the MCP server to your first working request is approximately 12 minutes. This compares to creating a custom script for the Twitter API, which may take you hours to read/study the documentation and several days to debug the errors that occur.

Scalability Without Going Crazy — The Twitter API has a strict limit on how many requests you can make; the MCP server will keep track of how many requests are remaining, queue requests, and when you exceed your limit, will indicate clearly: "Rate limit exceeded, Retry after 15:00". This prevents you from using your entire quota in one large burst request. It is especially important for those of us who are working on multiple projects.

Non-Coder Availability — Within ASCN.AI, the MCP server is built into the Automation Builder. Therefore, users can utilize the MCP server by selecting the "Twitter MCP" Node, entering their API Keys, and visually building their workflow without needing to be able to write code to accomplish monitoring brand mentions or publishing automatic analytical data.

Data Integration — When you have Twitter, Telegram, and On-Chain data; the MCP enables you to analyze it in a uniform manner. AI agents can send to X through an API (MCP) the real-time metrics of a crypto token monitored on Twitter and receive an increase in the number of token mentions within 10 seconds. Agents don't need to write adapters for each of their sources in order to create a summary.

Example Project: In October 2024, our team launched a monitoring service for Twitter on behalf of a crypto fund that tracks alpha signals of 120 tokens in real time. An MCP was created for X and filters were set up based on keywords and influencers. The MCP was then connected to our ASCN AI Assistant. During the first month, the MCP was able to process about 2000 tweets/hour, aggregate sentiment and send alerts directly to Telegram — all without requiring the client to write a single line of code. In that time frame, the fund was able to receive 14 signals to enter into positions and 3 signals produced a return on investment greater than 40%.

Step-by-Step Installation and Setup Guide for MCP

⚠️ NOTE: You will need access to the Twitter API in order to set up the MCP — this requires having a developer account on X, having an approved project as well as obtaining the necessary API keys. This process should take between a few hours and a few days to complete.

Installation steps

1. Ensure you have Node JS version 18 or greater installed by checking with the command line: node --version.
2. Clone or download the MCP Server's GitHub repository or install it via npm:
   git clone https://github.com/BioInfo/x-mcp-server.git; npm install
   or
   npm install -g twitter-mcp-server
3. Create a .env file containing your API Keys from the X Developer Portal:

TWITTER_API_KEY=Your API Key
TWITTER_API_SECRET=Your API Secret
TWITTER_ACCESS_TOKEN=Your Access Token
TWITTER_ACCESS_SECRET=Your Access Secret

4. To start the MCP server, type npm start into a command window; once the phrase "MCP Server running on port 3000" displays in the command terminal, your server is ready for use.
5. To confirm functionality of the MCP Server, perform a test of the get_user_info API Request using an MCP Client.

Configuration parameters for the platform include:

• Tools (server functions exposed to the client) such as search_tweets, post_tweet, like_tweet, etc.
• Rate limits that limit the number of requests that you can send in a specified time period (usually minutes or hours) and queues your requests if you reach your limit.
• Retry strategy which automatically retries requests on error with back off times that increase each time an error occurs.

When using no-code platforms (like ASCN.AI) configuration is much easier because you simply enter your API keys in their interface and select from pre-defined functions. The platform does all of the configuration work for you and deploys the server when everything is set up.

It should be noted that while the MCP server supports only those methods which are available under the Twitter API plan you have, on the free plan the get_full_archive_search method will return 403. There are specific endpoints included in your plan, so you should consult the Twitter API documentation to see which ones are available and what the limits are for each.

Potential real-world applications of the MCP and a no-code platform

Monitoring brand or token mentions

Configuration through ASCN.AI:

1. Create a workflow that triggers every 15 minutes.
2. Add a search_tweets node to your workflow using the following search term $SOL -is:retweet, and select the created_at, public_metrics, and author_id fields when building your search term.
3. Pass the returned JSON response from the search_tweets node to an AI agent for analysis of sentiment.
4. Use the AI agent to set up a notification for yourself when you receive a negative tweet from an account with more than 10,000 followers in your Telegram account.

Example Case: A cryptocurrency project received a large number of negative tweets following the failure of withdrawals. The MCP server was configured to look for specific keywords, and then an AI agent classified the tweets into three categories: Complaints, Questions and FUD. The first 6 hours saw 47 legitimate complaints identified, and 120 "panic" tweets identified. After identifying and addressing the first group of complaints, the project team was able to address the issue, rebuild trust in the project in 2 days and to not escalate those panic tweets.

The Automatic Publishing of Analytics

Workflow in ASCN.AI: At 9:00 AM UTC daily, Prices of tokens, Trading volume, and On-chain metrics are retrieved. Then an AI-generated digest is created, published by the post_tweet node.

Example Case: A Web3 startup needing a way to connect its MCP server to ASCN.AI without having any SMM staff available. The daily workflow consists of collecting news from CoinDesk and The Block, digesting this news using AI, publishing the news, and responding to frequently asked questions automatically. This led to a more than 700% increase in subscribers, from 300 to over 2400 subscribers, over a 4-month period, along with a decrease from 2 hours to only 10 minutes of time each day spent on managing the account and an increase of 35% engagement.

The MCP Server Compared to Other Solutions

Factors to Consider When Choosing MCP Server vs. Alternatives

Choose MCP Server if:

• You require custom automation beyond what is provided by a SaaS product.
• You are currently using AI agents and would like to integrate them natively.
• You require a stable legal access point without the risk of blocking.
• You are comfortable spending the time and effort to set it up or connect to ASCN.AI.

Choose an alternative if:

• You are in need of pre-made dashboards or other ready-to-use systems → LunarCrush or Santiment.
• You need a service that allows for no setup → Hootsuite or Buffer.
• You write code and want control over your solution → Tweepy.

Key Security Guidelines for MCP Server

• You should always add .env to your .gitignore file since many bots continuously scan GitHub for leaked keys.
• The encrypted token storage feature is available within ASCN.AI (the keys will be hidden in the ASCN.AI interface, and backups will be automatically created).
• Limit the permissions of the key to read-only if you don't need writing permissions.
• Rotate your keys every three to six months.
• Enable Two-Factor Authentication (2FA) for your X account.

Recent Updates on MCP Server

• December 2024 to January 2025: Release of MCP Server v1.2 with Spaces API support — transcription of audio spaces, analysis of topics discussed and quotes spoken during competitor AMAs.
• February 2025: Expansion of tweet analytics metrics — impressions, click-throughs and users demographic engagement.
• March 2025: A native integration with Claude 3.5 Sonnet (the ability to connect your MCP server to the Claude Desktop and use the search_tweets and post_tweet methods right out of the box).

Future Directions of the MCP Server

• Q2 2025 — A Streaming API will be available for live data streams.
• Q3 2025 — Multi-account Support (use multiple MCP servers with one ASCN.AI account) will be added to the product.
• Q4 2025 — Support for Bluesky and Mastodon.

MCP Server Help and Resources

• Official MCP Server Documentation: modelcontextprotocol.io
• GitHub Repository: Search "twitter mcp server" — Includes README with all instructions and examples for all methods available in MCP Server.
• ASCN.AI Knowledge Base: All documents (step-by-step) for ASCN.AI in Russian — ascn.ai/ru/documentation
• ASCN.AI Support: Reach out to ASCN.AI support via Telegram — @ascn_support or email them at support@ascn.ai (they will respond to you within 12 hours of your email).
• Discord Group for Anthropic Community — This is where you can discuss the MCP server and Claude.

Closing Remarks

The reason individuals who were unable to profit from the Flash Crash are different from those who profited is that those who lost money did not have automation and speed in their response. The MCP server provides the speed of reaction (from a signal of a tweet on Twitter to action) in seconds. If you're working within the cryptocurrency space, you will want to catch the alpha before anyone else. The MCP server — in conjunction with ASCN.AI — allows you to accomplish this goal quickly and effectively without the need for a team of developers.