Token Holder Analysis — Distribution & Whale Detection vs AWS MCP Servers

This capability analyzes the distribution of holders for any ERC-20 token by aggregating data from the blockchain to identify top holders, their percentage ownership, and the overall distribution structure. It utilizes a combination of on-chain data retrieval and statistical analysis to compute metrics such as the Gini concentration coefficient and whale counts, providing insights into the token's decentralization and potential risks. The integration with the x402 micropayment system allows for seamless access without requiring an API key, making it user-friendly for quick analyses.

Unique: Utilizes a micropayment model for access, allowing for low-cost, on-demand analysis without the need for API keys, which is uncommon in blockchain analytics tools.

vs alternatives: More cost-effective and accessible than traditional token analytics platforms that require subscriptions or API keys.

This capability identifies and counts the number of 'whales'—addresses holding a significant percentage of the total token supply—by analyzing the distribution of token holdings. It employs threshold-based logic to classify addresses as whales based on their ownership percentage, providing users with insights into potential market manipulation risks. The results are returned in a structured format, allowing for easy integration into other applications or analyses.

Unique: Offers a customizable whale definition based on user-defined thresholds, allowing for tailored risk assessments rather than a one-size-fits-all approach.

vs alternatives: More flexible in whale classification compared to static models used by other analytics tools.

This capability calculates the Gini concentration coefficient for the token's holder distribution, providing a quantitative measure of inequality among holders. It processes the distribution data to derive the coefficient, which indicates how concentrated the token ownership is. A higher Gini coefficient suggests greater inequality, which can signal potential risks for investors. The implementation leverages statistical formulas to ensure accuracy and reliability in the results.

Unique: Calculates the Gini coefficient specifically for ERC-20 tokens, providing a tailored metric that is not commonly available in standard token analysis tools.

vs alternatives: More focused on inequality measurement compared to general analytics platforms that may overlook this metric.

This capability assesses the trend of token holders over time, indicating whether the number of holders is growing or shrinking. It analyzes historical data to identify patterns in holder behavior, which can be crucial for understanding market sentiment and potential future price movements. The implementation involves tracking changes in holder counts and applying trend analysis algorithms to provide clear insights.

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

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

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awslabs/mcp | DeepWiki Loading... Index your code with Devin DeepWiki DeepWiki awslabs/mcp Index your code with Devin Edit Wiki Share Loading... Last indexed: 8 January 2026 ( 49d158 ) Overview What is Model Context Protocol? Available MCP Servers Server Workflow Classifications Architecture System Design Client-Server Interaction Package Structure & Dependencies Security & Permission Model Documentation System Core Infrastructure Core MCP Server AWS API MCP Server Lambda Handler & Remote Servers Infrastructure as Code Servers AWS IaC MCP Server Terraform MCP Server CDK MCP Server CloudFormation & Cloud Control Servers Container & Compute Servers ECS MCP Server EKS & Kubernetes Servers Lambda Tool MCP Server Serverless & Container Tools AI & Machine Learning Servers Bedrock KB Retrieval MCP Server Nova Canvas MCP Server SageMaker AI MCP Server AWS HealthOmics MCP Server Bedrock AgentCore & Other AI Servers Data & Analytics Servers DynamoDB MCP Server PostgreSQL MCP Server Other Database Servers S3 Tables & Storage Servers Analytics & Data Processing Servers Operations & Monitoring Serv