Jungle Grid MCP Server lets AI agents submit, estimate, monitor, and retrieve logs for GPU workloads through Jungle Grid. It enables agentic execution for inference, training, fine-tuning, and batch jobs without manually choosing GPU providers or infrastructure.
Enables agents to submit and manage persistent, dependency-aware task graphs with immutable artifacts, resource reservations, durable event streaming, and retryable process execution over MCP.
Enables coordinating specialist agents through an event-driven backend, allowing submission of goals, retrieval of job status and results, and listing of jobs via MCP tools.
Enables Kubernetes-native management of agent/model workloads via MCP tools, including fleet status, workload lifecycle, and boot orchestration for AI workflows.
Enables MCP clients to securely execute bounded coding tasks through registered backends, with idempotent job submission, status polling, and artifact retrieval. It isolates each job in Git worktrees and supports optional branch publishing and pull request creation under strict policy constraints.
