drainbrain-mcp-server

This capability scans a single token for rug pull risk, honeypot status, and temporal analysis using a machine learning model that evaluates various risk factors. It integrates with the DrainBrain API to fetch real-time data and applies a scoring algorithm that outputs a risk score from 0 to 100, indicating the likelihood of a rug pull. The implementation leverages a modular architecture that allows for easy updates to the risk assessment model as new data becomes available.

This capability allows users to scan up to 10 tokens in parallel, optimizing the risk assessment process by leveraging asynchronous API calls. The implementation uses a concurrent processing model to handle multiple requests simultaneously, significantly reducing the time required for bulk assessments. This design choice ensures that users can efficiently evaluate multiple investments at once without waiting for each token to be processed sequentially.

This capability checks the availability and health of the DrainBrain API and its underlying models by sending a ping request and evaluating the response time and status. It uses a simple HTTP request-response pattern to ensure that the service is operational before executing any risk assessments. This proactive approach helps users avoid wasting time on failed requests due to downtime or connectivity issues.

This capability allows users to compare the DrainBrain ML score against the RugCheck heuristic side-by-side. It pulls data from both sources and presents it in a structured format, enabling users to evaluate the strengths and weaknesses of each assessment method. The implementation uses a data aggregation pattern to compile results from both APIs, ensuring that users have a comprehensive view of the token's risk profile.