With the prosperity of the blockchain and the DeFi ecosystem, money flow activities in the blockchains are becoming increasingly frequent, complex, and diverse. The Money Flow Graph (MFG) serves as the foundation for various behavioral analysis, malicious activity detection, and money flow tracing tasks. However, traditional graph databases face the issue of storage requirement and performance when analyzing large-scale MFGs. In this work, we presentMFGScope, a lightweight domain-specific framework designed for graph-based analysis on EVM-compatible blockchains, with extensive optimizations for storage efficiency and query performance. The prototype ofMFGScopefor the Ethereum network achieves the storage of over 3 billion transfers and 1.7 billion relevant transactions in a single instance with less than 450 GB of disk usage. The evaluation shows that for common tasks,MFGScopeis more than 30 times faster and requires 78% less storage space than the commonly used graph database Neo4j. For the applications ofMFGScope, we present several use cases based on the MFG which cannot be performed efficiently using traditional graph databases and report interesting findings. To engage the community, the prototype ofMFGScopefor the Ethereum blockchain with the complete dataset will be open source.