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Connect Agent Workflow Bottleneck Analyzer to your AI

Set it up once and it works in ChatGPT, Claude, Cursor, and any other client you love. It all runs safely in the background, so you can relax and focus on what really matters. Pinpoint exactly where your agent workflows slow down.

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

ChatGPTClaudeCursorPerplexityGeminiMicrosoft CopilotRaycastMeta AI

Your AI does the work. We keep it safe and sound.

No other AI tool gives you this. Your workflow runs in the client you already love, your account stays protected, and you decide what the agent can and cannot do. It is the whole reason to make the switch.

Each Connector lives inside our infrastructure, the same place where your workflow runs. The three things below are already part of it, working quietly in the background so you never have to think about them. Nothing to add, nothing to set up, nothing to worry about.

A futuristic MCP client docking bay
V8 Isolate01

Each Connector runs inside its own sealed environment.

Each Connector runs inside its own sealed V8 runtime — no access to your files, your system, or other servers. A Connector never sees past its own request.

Kill Switch02

One switch stops every agent. Instantly.

One action deactivates every Connector, revokes every token, and ends every session at once. AI Governance keeps the record.

FinOps per User03

Spending limits that pause instead of surprise.

Every user keeps their own connections and credentials, isolated from everyone else. Anything that would cross your spending limit is held for your approval, not billed.

Three controls, built into the layer your AI connects through.

Curated set · 3 capabilities

We ship only the actions that matter.

Capability set01 / 01

01-03

3 capabilities in this set.

Part of 3 available through Agent Workflow Bottleneck Analyzer.

  1. 01

    Get optimization roadmap

    Ranks the stages in order of importance for engineering intervention

  2. 02

    Analyze pipeline health

    Provides a high-level diagnostic overview of the entire workflow

  3. 03

    Calculate stage impacts

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

Real work

What teams actually run

01

Debugging Slow Production Runs

A critical agent workflow suddenly slows down. Instead of checking every microservice, you run this MCP to immediately locate the specific stage responsible for the latency spike.

02

Meeting SLA Requirements

Your pipeline must complete in under 2 seconds. You use the MCP to calculate stage impacts and determine which stage needs optimization to meet the strict service level agreement.

03

Pre-Deployment Stress Testing

Before launching a major feature, you run a diagnostic check to predict potential bottlenecks and error risks under simulated high load.

04

Optimizing Cost and Speed

You need to reduce compute costs. The MCP helps you identify the most resource-intensive stages, allowing you to target optimization efforts for maximum efficiency gain.

One connector, every AI

Agent Workflow Bottleneck Analyzer works with the most popular AI clients.

These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.

ClaudeClaudeChatGPTChatGPTGeminiGeminiPerplexityPerplexityGrokGrokMicrosoft CopilotMicrosoft CopilotCursorCursorVS CodeVS CodeWindsurfWindsurfJetBrainsJetBrainsClineClineLangChainLangChainVercel AI SDKVercel AI SDKLovableLovableZ.aiZ.aiRaycastRaycastQwen CodeQwen CodeKimi CodeKimi CodeLe ChatLe Chat

Building your own app? The connector is yours to use.

You don't need a client to put Agent Workflow Bottleneck Analyzer to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.

Observed, not estimated

823ms average. Fast in production.

Agent Workflow Bottleneck Analyzer is checked daily against the live service.

Daily averagePeak 1026ms
Oct 3Today
Fastest day
736ms
Slowest day
1026ms
14-day trend
Improving-24%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 3 capabilities arrive ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of Agent Workflow Bottleneck Analyzer, so you can see the experience inside your AI.

It does not authenticate your account with Agent Workflow Bottleneck Analyzer. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

Agent Workflow Bottleneck Analyzer Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_NzGKm5rkMNG5wb3ArwX3srzCUizSp07mQuDJchM4/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — Agent Workflow Bottleneck Analyzer capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "agent-workflow-bottleneck-analyzer-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_NzGKm5rkMNG5wb3ArwX3srzCUizSp07mQuDJchM4/mcp"
    }
  }
}
  • Claude
  • ChatGPT
  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Step-by-step instructions for each client are in the guide. How to connect

Guided setup for Claude? Set up Agent Workflow Bottleneck Analyzer in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Agent Workflow Bottleneck Analyzer owners hand off.

This MCP is built for technical teams managing complex, multi-step AI workflows. If you're responsible for keeping agent pipelines reliable and fast, this capability gives you the data to prove where the bottlenecks are. It moves you past guesswork and straight to actionable engineering tasks.

  • 01

    DevOps Engineer

    Use this to monitor production pipelines, identify performance regressions, and validate service level agreements (SLAs).

  • 02

    ML Engineer

    Run diagnostics on agentic workflows to pinpoint which model call or data transformation step is causing unacceptable latency.

  • 03

    System Architect

    Model and analyze the theoretical performance of new pipeline designs before writing a single line of code.

FAQ

Questions Agent Workflow Bottleneck Analyzer owners ask.

  • 01

    Does this MCP handle Service Level Agreements (SLAs)?

    Yes. You can input your total SLA requirement, and the MCP will analyze the pipeline to determine if the current stage contributions cause the total latency to exceed that limit.

  • 02

    Is this for real-time monitoring?

    The MCP performs deterministic analysis on provided data sets. It analyzes the metrics you give it (like average latency and queue size) to diagnose potential issues, rather than monitoring a live stream.

  • 03

    What kind of pipelines can I analyze?

    It is designed for multi-stage agentic pipelines. This means any workflow that passes data through several distinct, sequential steps or services.

  • 04

    What is the difference between 'latency' and 'queue wait time'?

    Latency is the time spent actively processing data in a stage. Queue wait time is the time the data sits waiting in line before the stage can process it.

  • 05

    How does the analyzer identify the primary bottleneck?

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

  • 06

    Can I prioritize which stages to fix first?

    Yes, use get_optimization_roadmap to receive a prioritized list of stages based on their latency contribution and error rates.

  • 07

    What is error propagation risk?

    It is the calculated probability that a request successfully traverses all stages in the pipeline without encountering a failure.

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