Kubernetes Service Filter & Dependency Chain

Implement filters over Kubernetes service records, then compute dependency chains and query status along the chain. A final open-ended follow-up asks how to expose the code as an MCP server for agents while reducing noise. Requirements Given a collection of Kubernetes services with attributes, status, and dependencies: Implement filters by requested service attributes. Build or traverse dependency chains. Query status along a dependency chain. Handle missing dependencies and cycles. Discuss how this code would become a tool server for an agent and how the agent should avoid noisy output. Notes Use a service id -> service map for filtering and adjacency lists for dependencies. Dependency-chain queries should define direction: upstream dependencies vs downstream dependents. Cycle handling is important; use visiting / visited states or BFS with a seen set. The MCP follow-up is a design signal: expose narrow tools such as filter_services, get_dependency_chain, and summarize_status, return structured JSON, cap result sizes, and include confidence / truncation metadata so the agent does not flood users with raw cluster state. Preparation Implement attribute filtering with composable predicates. Implement DFS / BFS dependency traversal with cycle detection. Prepare a structured tool API with input schema, output schema, pagination, and error cases. Practice explaining noise controls: severity thresholds, result limits, dedupe, and summarization.