Agent Composition Pattern Calculator

Agent Composition Pattern Calculator MCP Connector for Claude

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Calculate execution plans, latencies, and efficiency for AI agent orchestration patterns.

3 tools Official Updated Oct 1, 2026 Official Vinkius Partner

This MCP server provides deterministic mathematical modeling for AI agent orchestration. It allows users to simulate various workflow patterns including sequential, parallel, pipeline, map_reduce, and router to determine the most efficient execution path. By analyzing agent chains, the tool calculates total latency, pattern efficiency, and identifies the bottleneck_agent to optimize performance based on goals like minimizing latency, maximizing quality, or minimizing cost.

latencyefficiencyworkflowagentsoptimization

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

evaluate_optimization_strategy

Compares different orchestration patterns to recommend the best one based on the user's specific goal

get_execution_plan

Generates the specific execution sequence and performance metrics for a given agent workflow

analyze_bottlenecks

Identifies specific points of failure or delay within a completed execution plan

See how to talk to your AI agent using Agent Composition Pattern Calculator.

Calculate the execution plan for a sequential workflow with three agents having latencies of 10, 20, and 15 seconds.

The total latency for this sequential workflow is 45 seconds.

What is the most efficient pattern for these agents: AgentA (10s), AgentB (10s), AgentC (10s) if I want to minimize latency?

The recommended pattern is parallel, with an expected latency of 10 seconds.

Identify the bottleneck in a pipeline workflow where Agent1 takes 5s, Agent2 takes 50s, and Agent3 takes 5s.

The primary bottleneck is Agent2, which contributes significantly to the total delay.

Use the `evaluate_optimization_strategy` tool. Provide your agent chain and your optimization goal, and it will simulate all patterns to recommend the best one.

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