Agentic Graph Analytics With External MCP Providers and Neo4j

The blog post discusses building modular graph analytics pipelines using Neo4j Aura Graph Analytics, a serverless cloud offering that can execute graph algorithms without relying on a Neo4j database. It highlights the ability to integrate external data sources, such as relational databases or CSV files, through the use of existing MCP servers like Supabase, thereby avoiding the need for individual integrations for each data provider. The post explores challenges in connecting large language models (LLMs) with external data sources while keeping raw data out of the LLM context to prevent overwhelming it. By grouping graph algorithms semantically, such as centrality and community detection, the system allows LLM agents to fetch data, run graph algorithms, and receive insights without directly handling data rows, thereby maintaining scalability and efficiency. The approach is demonstrated through a proof of concept where data from Supabase is used to run graph algorithms, showcasing the versatility and efficiency of dynamically mounting different MCP servers for various data sources.