Performance Regression Detector Connector for AI agents.
3 live capabilities
Monitor and predict LLM agent execution latency trends
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Why people use Performance Regression Detector
Stop chasing latency spikes with Performance Regression Detector
With this MCP, you stop guessing. You give your agent the ability to run its own statistical checks. It identifies the outliers and the trends for you, turning a mountain of execution timestamps into a clear picture of whether your system is healthy or heading for a crash.
What Vinkius changes
You stop guessing why your agents are slow and start using math to prove it.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 6,100+ Connectors
- Real-world use case 01
Post-deployment regression check
An engineer updates a complex prompt and uses analyze_execution_performance to ensure the new logic didn't double the execution time.
- Real-world use case 02
Preventing systematic slowdowns
An Ops engineer uses detect_performance_trend to catch a slow, steady increase in latency caused by growing context windows.
- Real-world use case 03
Capacity planning for agents
A developer uses predict_failure_horizon to determine when the current agent architecture will exceed the 30-second timeout limit.
Complete set · 3capabilities
The complete Performance Regression Detector capability set.
These are the exact actions your AI can choose when you ask it to work with Performance Regression Detector.
01—03
3 capabilities in this set.
Part of 3 available through Performance Regression Detector.
- 01 Capability
Predict failure horizon
Estimates how much longer the system can operate before execution times reach an unacceptable limit. It helps you plan maintenance before things break.
- 02 Capability
Analyze execution anomalies
Identifies specific execution instances that deviate significantly from historical performance
- 03 Capability
Calculate performance trend
Determines if the system is experiencing a systemic slowdown over time
Set up in minutes
One URL. Then ask Performance Regression Detector to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Performance Regression Detector from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_Mrc7VLVZnW2CJ3Vp0BfcGsW4A6xTf6bdZnQTBTMV/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it Performance Regression Detector, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Performance Regression Detector for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_Mrc7VLVZnW2CJ3Vp0BfcGsW4A6xTf6bdZnQTBTMV/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the Performance Regression Detector URL.
- Step 03
Save and start
Save the connection and enable Performance Regression Detector in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"performance-regression-detector": {
"url": "https://edge.vinkius.com/vk_preview_Mrc7VLVZnW2CJ3Vp0BfcGsW4A6xTf6bdZnQTBTMV/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using Performance Regression Detector
Open Agent mode in chat and ask: "Using Performance Regression Detector, help me...". 3 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"performance-regression-detector": {
"url": "https://edge.vinkius.com/vk_preview_Mrc7VLVZnW2CJ3Vp0BfcGsW4A6xTf6bdZnQTBTMV/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using Performance Regression Detector
Ask Copilot: "Using Performance Regression Detector, help me...". 3 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"performance-regression-detector": {
"url": "https://edge.vinkius.com/vk_preview_Mrc7VLVZnW2CJ3Vp0BfcGsW4A6xTf6bdZnQTBTMV/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using Performance Regression Detector
Open Cascade and ask: "Using Performance Regression Detector, help me...". 3 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"performance-regression-detector": {
"url": "https://edge.vinkius.com/vk_preview_Mrc7VLVZnW2CJ3Vp0BfcGsW4A6xTf6bdZnQTBTMV/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using Performance Regression Detector
Ask Cline: "Using Performance Regression Detector, help me...". 3 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add performance-regression-detector --transport http "https://edge.vinkius.com/vk_preview_Mrc7VLVZnW2CJ3Vp0BfcGsW4A6xTf6bdZnQTBTMV/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using Performance Regression Detector
Ask Claude: "Using Performance Regression Detector, show me...". 3 tools are ready
Where the request belongs
Work Performance Regression Detector can move forward.
This is for the engineers and ops teams who build agentic workflows and can't afford for their systems to become sluggish or unresponsive.
AI Engineer
Uses this to verify that new model prompts or logic changes haven't tanked execution speed.
LLM Ops Engineer
Monitors long-term performance trends to prevent systematic degradation in production environments.
Backend Developer
Checks if agent latency is impacting the overall application response time.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
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Traffic Manager Prover
A startup spent $180K on Meta Ads and reported ROAS 4.2x. The board celebrated. Then someone ran an incrementality test. a 10% holdout that saw no ads. 38% of 'attributed' conversions were organic users who would have purchased anyway. True incremental ROAS: 2.6x. $68K spent on people who needed no convincing. Platform-reported ROAS is fiction. This capability forces five axes: unit economics per channel, attribution integrity with incrementality testing, funnel diagnostics at every stage, creative performance with fatigue analysis, and audience architecture with saturation awareness.
Langfuse Trace URL Extractor
Extract trace IDs and construct observability URLs from Langfuse payloads.
X Ads (Twitter)
Connect your X Ads account to any AI agent. audit campaigns, analyze line item performance, and pull engagement reports through natural conversation.
Box
Store, share, and collaborate on files securely with enterprise-grade cloud content management and governance controls.
Bring your own AI
Change the model, client or framework. Keep Performance Regression Detector connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about Performance Regression Detector.
The practical details behind the request, access and result.
How can the Performance Regression Detector MCP help me monitor my AI agents?
It gives your agent the ability to perform statistical analysis on its own execution times, allowing it to spot outliers and predict when it might become too slow to function.
Can I use Performance Regression Detector MCP to find why my agent is slow?
Yes. It helps you distinguish between random one-off delays and systematic performance decay, making it much easier to pinpoint if a recent change caused a regression.
How does Performance Regression Detector MCP predict future latency issues?
It uses linear regression on your recent execution data to calculate a trend line, which it then uses to estimate when you will hit specific time thresholds.
Is Performance Regression Detector MCP useful for production monitoring?
Absolutely. It's designed for engineers who need to maintain stable latency in production by catching degradation trends before they turn into system failures.
Does Performance Regression Detector MCP work with any AI client?
Yes, it works with any MCP-compatible client like Claude, Cursor, or Windsurf, as long as you have it connected through Vinkius.
How is a performance regression defined?
A regression is flagged when an execution time exceeds the baseline median plus two times the baseline standard deviation.
Can I predict when my agent will hit a performance limit?
Yes, by using predict_failure_horizon, you can estimate the number of steps remaining before execution times reach a specified critical limit based on current trends.
What data is required for analysis?
The capabilities require a chronological list of execution durations in seconds provided via the executionTimeHistory parameter.
How does the capability identify a regression?
An execution is flagged as a regression if its duration exceeds the baseline median plus two times the baseline standard deviation.
Can I predict when my agent will hit a latency limit?
Yes, by using predict_failure_horizon, you can estimate the number of execution cycles remaining before a critical latency limit is breached based on current trends.
What is the purpose of the trend analysis?
The calculate_performance_trend capability uses linear regression to determine if the system is experiencing a systemic slowdown over time.
One connection away
Give your agent a direct line to Performance Regression Detector.
Connect Performance Regression Detector once. Keep it beside 6,100+ managed Connectors when the next task needs more.
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