Xpoz MCP acts as a bridge between AI assistants and the vast world of social media, allowing agents to "read" and analyze platforms like X (Twitter) and Instagram using plain English. Instead of manually searching for posts or scrolling through feeds, users can simply ask their AI to find specific trends, track brand mentions, or gather public sentiment. It transforms social media into a searchable database that Claude and other LLMs can access directly, making it easy to get real-time insights without ever leaving the chat interface. For developers and researchers, this tool provides a powerful infrastructure that handles over 1.5 billion cached posts and real-time updates without the headache of managing individual platform API keys or navigating complex rate limits. The server is specifically designed for AI integration, featuring over 30 data fields including engagement metrics, geographic data, and media URLs. It enables sophisticated tasks like deep-dive competitor intelligence, influencer discovery, and large-scale market research by retrieving structured data that AI models can immediately process and summarize. On a technical level, the Xpoz MCP is performance-optimized to work seamlessly within the constraints of LLM context windows. It utilizes intelligent sub-second caching and batch pagination to ensure that queries are both fast and data-efficient. Developers can leverage advanced features like field selection to pull only the necessary data points—minimizing token usage—and can even trigger a "real-time mode" to bypass caches for the most current information. Whether integrated via a desktop client or through command-line tools, this server offers a robust, production-ready pipeline for social media intelligence.
1. Installation For Claude.ai Web / Claude Desktop (via UI): 1. Navigate to Settings → Connectors. 2. Click Add Connector. 3. Use the following details: - Name:Xpoz Social Intelligence - URL:https://mcp.xpoz.ai/mcp 4. Complete authentication at Settings → Connectors → Xpoz → Connect. For Claude Code (CLI): Run the following command in your terminal: bash claude mcp add --transport http --scope user xpoz-mcp https://mcp.xpoz.ai/mcp --header "Authorization: Bearer [your Xpoz API token]" ---
2. Configuration To manually configure the Xpoz MCP server (e.g., for Claude Desktop or other MCP-compatible clients using the HTTP transport), you can add the following to your configuration file: json { "mcpServers": { "xpoz-mcp": { "command": "curl", "args": [ "-s", "-X", "POST", "https://mcp.xpoz.ai/mcp", "-H", "Authorization: Bearer [your Xpoz API token]", "-H", "Content-Type: application/json" ] } } }Note: Ensure you replace [your Xpoz API token] with your actual token obtained from xpoz.ai. ---
3. Available Tools Twitter/X Tools: * User Intelligence: Retrieve profiles by username/ID or search for users. * Content Discovery: Search posts by keywords or fetch timelines from specific users. * Engagement Analysis: Retrieve comments, quote tweets, and retweets. * Analytics: Count tweets based on specific criteria and track trends over time. Instagram Tools: * Profile Intelligence: Fetch profiles and search for users. * Content Discovery: Search posts by keywords and retrieve user timelines. * Engagement Analysis: Extract comments and nested replies. TikTok Tools: * Coming soon (Q1 2026). ---
4. Example Prompts * "Find tweets about AI safety from researchers in the last week" * "Get Instagram posts from @natgeo with engagement metrics" * "Count tweets mentioning 'climate change' by month" * "Show me all replies to this viral tweet" * "Find Instagram influencers in the fitness niche" * "Get geographic data for tweets about the election"
What you can do with Xpoz MCP
Use Case 1: Competitor Benchmarking and Content Strategy Problem: Marketing teams often struggle to manually track the performance of competitors across different platforms, making it difficult to understand which content types are driving engagement in their industry. Solution: The Xpoz MCP allows AI agents to aggregate engagement metrics (likes, retweets, comments) from specific competitor profiles on Twitter and Instagram. By querying this data via natural language, a team can generate a weekly report comparing their own performance against rivals without manual data scraping. Example: Ask Claude, "Compare the engagement rates of the last 10 Instagram posts from @CompetitorA and @CompetitorB. Which topics received the most comments?"
Use Case 2: Hyper-Local Market Sentiment Analysis Problem: Businesses launching products in specific regions need to understand local discourse and consumer sentiment, but broad social media searches often return global results that lack geographic context. Solution: Since Xpoz provides access to geographic data (country, region, city) for posts, an AI agent can filter social media conversations to a specific location. This helps researchers identify local pain points or trends relevant to a specific city or region. Example: Use the query, "Find recent tweets from users in Austin, Texas, mentioning 'public transit' or 'traffic' to identify current local frustrations for our urban planning app."
Use Case 3: Identifying Niche Influencers for PR Campaigns Problem: PR agencies often rely on expensive influencer databases that might miss rising "micro-influencers" who have high authority in specific technical or academic niches. Solution: Xpoz’s User Intelligence and Content Discovery tools allow users to search for creators based on their bios, professional credentials, and specific keywords in their posts. This enables the discovery of subject matter experts rather than just high-follower accounts. Example: "Find 10 Twitter users who mention 'AI safety' in their bio and have posted about 'neural alignment' in the last 30 days, then list their follower counts and average engagement."
Use Case 4: Rapid Crisis Response and Origin Tracking Problem: When a negative trend or PR crisis starts, it is difficult to find the "ground zero" post and understand how the sentiment is spreading through replies and quote tweets. Solution: Using the Engagement Analysis tools, an AI agent can retrieve the full thread of replies and quote tweets for a specific viral post. It can then analyze these replies to identify the most influential voices contributing to the narrative and determine if the sentiment…