MCP For Claude Marketplace
Multi-Agent Parallelization Optimizer

Multi-Agent Parallelization Optimizer MCP Connector for Claude

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Optimize agent workflow execution timing and resource efficiency.

3 tools Official Updated Oct 1, 2026 Official Vinkius Partner
More Details Connect to Claude

This MCP server provides a deterministic engine to optimize the execution timing and resource efficiency of multi-agent workflows. By analyzing task dependencies and communication costs, it calculates critical path durations, worker utilization, and speedup ratios. Use analyze_workflow_efficiency to find the optimal worker count for your specific task set, or find_critical_path_bottlenecks to identify tasks delaying your workflow. It also includes simulate_scaling_impact to predict how adding more workers will affect performance.

agentsparallelizationefficiencyschedulingworkflow-optimization

3 tools expose this connector's capabilities to your AI agent.

analyze_workflow_efficiency

Evaluates the performance metrics of a specific agent workflow configuration

find_critical_path_bottlenecks

Identifies which specific tasks are preventing the workflow from finishing faster

simulate_scaling_impact

Predicts how increasing the number of workers will affect the total execution time and speedup

See how to talk to your AI agent using Multi-Agent Parallelization Optimizer.

Analyze the efficiency of these tasks: [{'task_id': 'A', 'duration_ms': 100, 'dependencies': []}, {'task_id': 'B', 'duration_ms': 200, 'dependencies': ['A']}] with 2 workers and 10ms overhead.

The critical path duration is 300ms, the actual duration with 2 workers is 300ms, and the speedup ratio is 1.0.

Which tasks are the bottlenecks in this workflow: [{'task_id': '1', 'duration_ms': 50, 'dependencies': []}, {'task_id': '2', 'duration_ms': 150, 'dependencies': ['1']}]?

The bottleneck tasks are task 1 and task 2, with a total critical path duration of 200ms.

Simulate scaling for these tasks up to 5 workers with 5ms overhead: [{'task_id': 'T1', 'duration_ms': 100, 'dependencies': []}, {'task_id': 'T2', 'duration_ms': 100, 'dependencies': []}]

With 1 worker, the duration is 200ms. With 2 workers, the duration is 105ms due to communication overhead.

You can use the `analyze_workflow_efficiency` tool. It calculates the `optimal_worker_count`, which is the point where adding more workers provides less than a 10% improvement in duration.

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