Xpoz - Connect Your AI Agents to Social Media

What

Xpoz is a social intelligence product for AI agents and developers. It exposes live and historical social media data through a remote MCP server and typed SDKs, so language models can search, filter, and analyze posts, profiles, trends, and engagement using natural language or code.

The product appears positioned as a lower-friction alternative to direct social platform APIs and manual scraping. It serves teams doing social listening, research, marketing intelligence, competitive analysis, and agent development, with support for Claude, ChatGPT, OpenClaw, and custom applications via TypeScript and Python packages.

Features

• Remote MCP server for social search — Lets MCP-compatible AI assistants query social media data without building custom API connectors from scratch.
• Natural-language querying across platforms — Supports searches for topics, users, hashtags, sentiment, and trends across Twitter/X, Instagram, TikTok, and Reddit.
• Historical and live social data access — Combines current posts and metrics with historical coverage to support trend tracking, narrative analysis, and longitudinal research.
• Structured filtering and pagination — Includes filters for date, author, language, location, and engagement metrics, plus pagination for large result sets.
• Profile and engagement intelligence — Provides access to profile information, follower metrics, verification status, and engagement patterns for account-level analysis.
• TypeScript and Python SDKs — Offers typed packages for developers building custom pipelines, dashboards, or automations outside an AI assistant interface.

Helpful Tips

• Validate platform depth by use case — The site confirms support for four social platforms, but implementation depth may vary, so teams should verify field coverage and query behavior for their specific workflows.
• Use MCP for exploratory work, SDKs for production pipelines — Natural-language access is well suited to analyst and agent workflows, while typed SDKs are likely the better fit for repeatable backend processes.
• Check data governance needs early — The page describes OAuth, token management, and commercial use, but it does not provide detailed governance, retention, or compliance documentation on this page.
• Design queries around filters and result volume — Since the product supports large-scale retrieval, teams should define time windows, engagement thresholds, and platform scope to keep analysis focused and efficient.
• Test agent reliability on nuanced analysis tasks — Xpoz can provide the social data layer, but output quality for sentiment, threat detection, or narrative analysis will still depend on the LLM and workflow design.

OpenClaw Skills

Xpoz is a strong fit for the OpenClaw ecosystem because it provides an external social data layer that agents can query in real time through MCP or likely through SDK-driven workflows. Within OpenClaw, this could support skills for brand monitoring, trend detection, influencer discovery, competitor watchlists, campaign tracking, and social research brief generation. The site explicitly shows OpenClaw in its installation options, which suggests practical compatibility for agent-based usage.

A likely OpenClaw use case is a multi-step social intelligence agent that gathers posts on a topic, filters by engagement and date, summarizes sentiment shifts, maps key accounts, and produces daily reports or triggers follow-up actions. In security and policy contexts, similar workflows could likely be adapted for coordinated-campaign monitoring or narrative tracking, although that would depend on the surrounding OpenClaw workflow design rather than a confirmed native Xpoz feature set beyond data access and filtering.