Agent Workflow Bottleneck Analyzer

Agent Workflow Bottleneck Analyzer MCP Connector for Claude

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Identifies performance bottlenecks and error risks in agentic pipelines.

3 tools Official Updated Oct 1, 2026 Official Vinkius Partner

This MCP server provides deterministic analysis of multi-stage agentic pipelines. It identifies the primary analyze_pipeline_health bottleneck, calculates stage contributions to latency, evaluates queue wait times, and determines error propagation risks. Use get_optimization_roadmap to prioritize engineering efforts and calculate_stage_impacts for detailed performance breakdowns.

pipelinelatencybottleneckoptimizationreliability

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

get_optimization_roadmap

Ranks the stages in order of importance for engineering intervention

analyze_pipeline_health

Provides a high-level diagnostic overview of the entire workflow

calculate_stage_impacts

Breaks down the specific contribution of each stage to the total latency and cumulative error risk

See how to talk to your AI agent using Agent Workflow Bottleneck Analyzer.

Analyze this pipeline: stages=[{name: 'Ingestion', avg_latency_ms: 100, p99_latency_ms: 200, error_rate: 0.01, queue_size: 5}, {name: 'Processing', avg_latency_ms: 500, p99_latency_ms: 1200, error_rate: 0.05, queue_size: 2}], total_sla_ms: 2000

The primary bottleneck is Processing. The total latency exceeds the SLA. Recommended action: Optimize This Stage.

What is the optimization priority for a pipeline where 'Data Fetch' has high latency and error rate?

The optimization priority will list 'Data Fetch' at the top if its contribution to latency multiplied by its error rate is the highest.

Check the queue wait time for a stage with 10 items in queue and 50ms average latency.

The queue wait time for that stage is 500ms.

The `analyze_pipeline_health` tool identifies the bottleneck by finding the stage with the highest ratio of P99 latency to the total allowed SLA.

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