Preventing Context Overload: Controlled Neo4j MCP Cypher Responses for LLMs

Connecting large language models to a Neo4j graph database allows for dynamic Cypher query generation, but this can lead to context overload if the query results are too large or complex. Unchecked responses can bloat the model's context window with excessive data, reducing the quality of subsequent reasoning in multi-step agent workflows. To prevent this, several control mechanisms are proposed: implementing query timeouts, sanitizing results to remove noisy data like embedding vectors, and using token-aware truncation to limit the response size. Using a more compact YAML response format instead of JSON can also help reduce token count and improve latency.