Xpoz Review
Natural language queries across 1.5B+ social posts via API and MCP integration
Xpoz is the most developer-friendly social intelligence tool in its price range. The MCP integration and natural language query interface remove the need for Boolean filter expertise, while the credit model keeps costs low for teams with variable monitoring needs. The trade-off is shallower dedicated monitoring compared to tools built solely around continuous alerting.
Pros and cons
- Natural language queries mean no Boolean filter syntax to learn or maintain
- MCP integration lets you query social data directly from Claude, Cursor, or any compatible AI client
- Credit-based pricing scales with actual usage rather than seat count
- Free tier with 2,500 credits lets you test real coverage before committing
- Access to 1.5B+ posts across Twitter, Instagram, TikTok, and Reddit in one interface
- Credit costs add up fast for continuous high-volume monitoring
- No white-label tier or client-sharing features for agencies
- Platform coverage is limited to four networks: no Hacker News, LinkedIn, YouTube, or GitHub
- No persistent real-time alerting in the same sense as dedicated monitoring tools
- Support documentation and community resources are still thin for a newer platform
What is Xpoz?
Xpoz is a social media intelligence platform that lets you query a database of over 1.5 billion posts from Twitter, Instagram, TikTok, and Reddit using plain English rather than Boolean operators. You ask a question, and it returns relevant posts with context, sentiment, and engagement data attached.
The product is built with AI workflows in mind. Its MCP server makes social post data queryable from within Claude, Cursor, or any other LLM environment that supports the Model Context Protocol. This is useful for product teams that want to incorporate customer voice into their AI-assisted research without leaving their primary tools.
Xpoz uses a credit-based billing model where each query or data pull consumes credits. The free tier provides 2,500 credits to test the service, and paid plans start at $20/month. This makes it affordable for teams with episodic research needs but less predictable for teams running continuous background monitoring.
Core features
Natural language social data queries
Instead of constructing Boolean queries with AND/OR/NOT operators, you describe what you're looking for in plain English. Xpoz interprets the intent and retrieves matching posts from its 1.5B+ post database. This removes a significant skill barrier for non-technical researchers.
MCP server integration with Claude and other LLMs
The Xpoz MCP server exposes its social query capabilities as a tool accessible from any MCP-compatible AI environment. Product managers and researchers can pull social data into their Claude conversations without switching context to a separate dashboard.
Multi-platform coverage across Twitter, Instagram, TikTok, and Reddit
All four major consumer social platforms are searchable from a single interface. Queries return post text, engagement metrics, author information, and timestamps. Historical data depth varies by platform but typically covers months of posts.
Brand monitoring with sentiment and relevance scoring
Set up brand queries to surface mentions alongside sentiment scoring. Results are ranked by relevance rather than recency, so high-signal posts surface ahead of low-engagement noise even if they are older.
REST API for programmatic data access
The API exposes the same query capabilities as the web interface. Credit consumption applies equally to API calls, making it straightforward to integrate Xpoz data into dashboards, scripts, or automated research workflows.
Pricing
| Feature | Free 0 | Pro $20/mo | Max $200/mo |
|---|---|---|---|
| Credits included | 2,500 | 30,000 | 600,000 |
| Platform coverage | 4 platforms | 4 platforms | 4 platforms |
| REST API access | ✓ | ✓ | ✓ |
| MCP server | ✓ | ✓ | ✓ |
| Natural language queries | ✓ | ✓ | ✓ |
| White-label / client sharing | ✗ | ✗ | ✗ |
| Priority support | ✗ | ✗ | ✓ |
Who it is for
Teams that periodically need to understand what customers are saying about their category, competitors, or pain points benefit most from the natural language interface and MCP integration. It fits into research sprints rather than continuous monitoring workflows.
The free tier and low entry price make Xpoz accessible for individual builders who want to monitor brand mentions and category conversations without committing to expensive enterprise tools.
The MCP server makes Xpoz a natural data source for AI-assisted research products. Engineers building tools on top of Claude or similar models can pull live social data as context without building their own scraping infrastructure.
Verdict
Xpoz is the right tool for teams that need on-demand social intelligence rather than continuous monitoring. The MCP integration is its clearest differentiator and makes it genuinely useful inside AI-native workflows.
Frequently asked questions
What counts as a credit in Xpoz?
Credits are consumed per query and per data point returned. Simple queries use fewer credits; large result sets or high-frequency monitoring uses more. The platform shows estimated credit consumption before you confirm a query so you can adjust scope if needed.
Does Xpoz support GitHub or Hacker News monitoring?
No. Current coverage is limited to Twitter, Instagram, TikTok, and Reddit. If developer community sources like Hacker News or GitHub are important to your monitoring strategy, look at Octolens instead.
How does the MCP integration work?
You connect your Xpoz account to a compatible AI environment using the MCP configuration Xpoz provides. Once connected, the social query tool appears as an available function in your Claude or Cursor session. You can ask questions in natural language and receive social data as structured context within your conversation.
Can I use Xpoz for continuous monitoring with real-time alerts?
Xpoz is better suited for on-demand queries than persistent monitoring. While you can set up recurring searches, it doesn't have the same real-time alerting infrastructure as tools like Syften or Octolens that are purpose-built for instant notifications.
Is the data current or historical?
Xpoz indexes recent posts and maintains historical data going back several months depending on the platform. For Twitter and Reddit the coverage is reasonably deep. TikTok and Instagram data coverage may be more limited due to platform API restrictions.