Holder Trend Analysis

Token holder distribution API for AI agents. Analyze any ERC-20 token's holder structure: top holders with % ownership, whale count, Gini concentration coefficient, holder trend (growing/shrinking), and smart money addresses. Tools: token_get_holder_analysis. Use this for due diligence before inve

Unique: Provides a dynamic view of holder trends over time, which is often overlooked in static analyses of token distributions.

vs others: More focused on temporal analysis compared to competitors that only provide snapshot data.

via “longitudinal trend analysis”

I spent years building a 103B-token Usenet corpus (1980–2013) and finally documented it [P]

Unique: Combines extensive historical data with advanced statistical analysis tools to facilitate in-depth trend analysis that is often overlooked in smaller datasets.

vs others: More comprehensive in tracking long-term trends compared to datasets that only cover recent social media interactions.

via “trend tracking over time”

Connect to your Oura Ring data to retrieve sleep, activity, readiness, heart rate, stress, and workout metrics. Analyze recent sleep patterns, summarize activity, and check recovery status with clear, actionable insights. Track trends over time and bring your wellness metrics into your workflows.

Unique: Utilizes time-series analysis to create dynamic visualizations, making it easier for users to interpret their health data over time.

vs others: More effective than static reports that do not provide visual context for data changes.

via “market trend analysis”

AI-powered business intelligence MCP server. 7 tools for competitive analysis, company research, market trends, news monitoring, lead discovery, and industry insights. Real-time data from multiple intelligence sources.

Unique: Combines statistical analysis with NLP for sentiment insights, providing a deeper understanding of market trends compared to standard analytics tools.

vs others: Offers richer insights than traditional tools by integrating sentiment analysis into market trend evaluations.

via “trend detection and topic clustering from social media streams”

MCP server: social-listening

Unique: Implements trend detection as an MCP tool that operates on aggregated social media data, enabling Claude to discover emerging topics and incorporate trend insights into reasoning and planning. Provides time-series trend velocity metrics, allowing clients to distinguish between sustained trends and fleeting spikes.

vs others: More actionable than generic trend APIs because it integrates with the social-listening search pipeline, allowing clients to drill down from trend discovery to specific posts and sentiment. Provides trend lifecycle data (emergence, peak, decay) that most real-time trend tools don't expose.

via “historical-trend-tracking”

via “trend-momentum-tracking”

via “time-series-and-trend-analysis”

via “real-time trend emergence detection and ranking”

Unique: Combines mention velocity, sentiment acceleration, and engagement metrics into a composite trend score rather than relying on single-signal detection; likely uses market-regime-aware baselines that adjust for bull/bear/sideways conditions

vs others: More responsive than traditional technical analysis indicators which lag price by definition, but less predictive than institutional order flow analysis or options market positioning data

via “historical trend analysis and pattern recognition”

via “team engagement trend tracking”

via “financial-trend-analysis”

via “time-series-financial-trend-analysis”

via “research trend analysis”

via “historical trend analysis and forecasting”

via “engagement trend analysis and anomaly detection”

Unique: Applies time-series analysis to engagement metrics rather than treating each snapshot independently. This enables detection of gradual trends (slow burnout buildup) and sudden anomalies (post-event engagement drops). The system likely uses statistical baselines (e.g., moving averages, standard deviations) rather than fixed thresholds.

vs others: More sophisticated than static dashboards (Tableau, Power BI) that show current metrics, but less advanced than specialized time-series analytics platforms (Datadog, New Relic) that use machine learning for anomaly detection.

via “trend-and-time-series-analysis”

via “feedback trend tracking over time”

via “trend identification from discussions”

via “trend-analysis-and-time-series-visualization”