This is a production-level medical agent server template based on the AOP protocol. It can deploy multiple professional medical agents as MCP tools, supporting the discovery and invocation of various medical analysis agents (such as laboratory data interpretation, ICD-10 coding, treatment plans, drug interactions, imaging triage, and clinical note summarization) through MCP clients, and providing functions such as queue management, dynamic discovery, and batch processing.

Auto Causal Inference is a project that uses large language models (LLMs) to automatically perform causal inference. Users only need to specify the treatment variable and the outcome variable, and the system can automatically complete the full - process analysis, including variable role identification, causal graph construction, effect estimation, and model validation. The project provides two agent architectures (LangGraph and MCP) to achieve this function, which is particularly suitable for causal problem analysis in the banking scenario.

The Eka MCP Server is a medical knowledge base interaction service developed by Eka Care, focusing on the Indian medical scenario. It provides a structured query function for drug brands and treatment plans to improve the accuracy and reliability of AI medical responses.