Social Intelligence for AI Agents
Give your AI agents the ability to understand social media conversations, identify influencers, and analyze audience sentiment—all through natural language queries.
The Problem
Building AI agents that can access social media data has traditionally been a significant technical challenge:
API complexity
Twitter and Instagram APIs require extensive authentication setup, rate limit management, and endpoint-specific code
Data access barriers
Official APIs have become increasingly restrictive and expensive, limiting what independent developers and researchers can build
Integration overhead
Connecting social data to LLMs requires building custom data pipelines, parsing responses, and handling pagination
No semantic understanding
Raw API responses need additional processing before AI can reason about social trends, sentiment, or influence
The Workflow
The Model Context Protocol (MCP) changes how AI agents access external data. With Xpoz MCP, your Claude-powered agents can query Twitter and Instagram data through natural language, receiving structured responses ready for analysis.
Example Queries
Ask Claude in natural language. Here are some examples with the underlying API calls:
Find users leading conversations on a topic
>"Find Twitter users who have posted about "generative AI" in the past month, sorted by total engagement on their relevant posts. Tool: getTwitterUsersByKeywords Fields: username, name, followersCount, relevantTweetsCount, relevantTweetsLikesSum"
Analyze how content spreads
>"Show me who retweeted and quoted this viral post about climate tech, including their follower counts to understand amplification patterns. Tool: getTwitterPostInteractingUsers Interaction types: retweeters, quoters Fields: username, followersCount, isVerified, description"
Monitor brand sentiment in real-time
>"Search Instagram posts mentioning our brand name, including engagement metrics and comment counts to gauge reception. Tool: getInstagramPostsByKeywords Fields: id, caption, username, likeCount, commentCount, createdAtDate"
Detect potentially inauthentic accounts
>"Get the profile for this account with authenticity scoring to assess whether engagement might be artificial. Tool: getTwitterUser Fields: username, followersCount, isInauthentic, isInauthenticProbScore, inauthenticType"
Why XPOZ
Native LLM integration
Built for the Model Context Protocol, meaning Claude and other MCP-compatible models can use it without custom integration code
No API key management
You don't need to obtain, secure, or rotate Twitter or Instagram API credentials
Handles scale automatically
Pagination, rate limiting, and data freshness are managed server-side—your agent just requests what it needs
Structured for reasoning
Responses include the fields AI needs for analysis, from engagement metrics to authenticity scores
CSV export for deep analysis
Large datasets can be exported for statistical analysis beyond what fits in context
Frequently Asked Questions
No. Xpoz MCP handles all data access. You authenticate with Xpoz via Google, and the MCP server manages the underlying data retrieval. There are no API keys to configure or rate limits to monitor.
Xpoz implements the Model Context Protocol standard. Claude (via Claude.ai, Claude Desktop, or Claude Code) can connect to Xpoz as a remote MCP server. Your agent then has access to social media tools it can call during conversations.
Xpoz maintains cached data that auto-refreshes when older than one week. For time-sensitive queries, you can request fresh data explicitly. Coverage varies by account and platform—the system returns what's available and indicates when data is partial.
Yes. Most query tools include a CSV export option. After running a query, you receive a download link for the complete dataset, useful for statistical analysis, visualization, or archival.
Get Started
Connect Xpoz MCP to your Claude environment and start building social-aware AI agents:
Claude.ai Web/Desktop: Settings → Connectors → Add custom connector → Enter `https://mcp.xpoz.ai/mcp`
Claude Code: Run `claude mcp add --transport http --scope user xpoz https://mcp.xpoz.ai/mcp`
Setup takes about two minutes. A free trial tier lets you explore the capabilities before committing. For detailed setup instructions and advanced use cases, visit the [Xpoz Help Center](https://help.xpoz.ai). Your AI agents are ready to understand the social web.
Related Use Cases
Find Buyers Before They Find Competitors
Build AI-powered sales agents that detect buying intent signals across social media and qualify leads in real-time—before your competitors even know they exist.
MarketingFind Micro-Influencers in Any Niche
Discover authentic micro-influencers with engaged audiences in any vertical. Transform the needle-in-a-haystack search into a systematic, data-driven process that surfaces genuine voices in your target market.
MarketingFind Perfect Influencers Before Your Competitors Do
Build an AI-powered influencer discovery system that identifies authentic creators with engaged audiences—using natural language queries instead of complex API integrations.
Ready to Build Your Social Intelligence for AI Agents?
Get started with 100,000 free results per month. No credit card required.