Evaluating Graph Retrieval in MCP Agentic Systems

The blog post discusses a framework for evaluating retrieval quality in Model Context Protocol (MCP) agentic systems, particularly focusing on how these systems interact with graph databases like Neo4j. It highlights the need to move beyond traditional single-step Cypher query evaluations to a more dynamic, multi-step reasoning approach, which better reflects real-world agent interactions that involve iterative processing and exploration of data. The article introduces a new benchmark designed to assess the quality of final answers produced by agents using an agentic approach, incorporating real-world complexities such as typographical errors and informal language. This evaluation benchmark, developed using Claude 4.0 and hosted via LangChain, signifies a shift towards measuring the semantic quality of results rather than just technical query accuracy, emphasizing the importance of concise, accurate answers over sheer retrieval capability. The results of the evaluation indicate that while agents can effectively handle complex queries using the MCP-Neo4j-Cypher interface, performance is impacted by factors such as input noise and question complexity, with potential improvements suggested through enhancing schema access and refining retrieval strategies.

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