Retry Executor Connector for AI agents.
3 live capabilities
Automate error recovery and rate limit handling for agentic workflows
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Why people use Retry Executor
Stop agent failures with Retry with Backoff Executor error handling
With this MCP, that frustration disappears. Instead of your agent hitting a wall, it follows a calculated path to recovery. It waits, it adjusts, and it tries again at the perfect moment. You get a resilient system that handles the messy reality of the web without you having to write custom retry loops for every single capability call.
What Vinkius changes
Get the Retry Executor answer you need in the AI client you already use.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 6,100+ Connectors
- Real-world use case 01
Recovering from LLM rate limits
An agent hits a rate limit while processing a large batch of data.
- Real-world use case 02
Handling unstable third-party APIs
A workflow calls a weather API that occasionally returns 500 errors.
- Real-world use case 03
Scaling agentic swarms
Multiple agents are running simultaneously.
Complete set · 3capabilities
The complete Retry Executor capability set.
These are the exact actions your AI can choose when you ask it to work with Retry Executor.
01—03
3 capabilities in this set.
Part of 3 available through Retry Executor.
- 01 Capability
Calculate next attempt
Figures out if a retry is allowed and how many seconds to wait. It prevents your agent from spamming a failing service.
- 02 Capability
Get retry status
Shows the current attempt count and the history of errors. It helps you see how close an agent is to giving up.
- 03 Capability
Validate retry configuration
Checks your backoff math to ensure it's logical. It prevents you from setting up impossible retry loops.
Set up in minutes
One URL. Then ask Retry Executor to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Retry Executor 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_SHWGIdaaci5qo0tOVUAXOHAjDWyUnJ4tWOEU1eiW/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 Retry Executor, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Retry Executor for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_SHWGIdaaci5qo0tOVUAXOHAjDWyUnJ4tWOEU1eiW/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 Retry Executor URL.
- Step 03
Save and start
Save the connection and enable Retry Executor in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"retry-with-backoff-executor": {
"url": "https://edge.vinkius.com/vk_preview_SHWGIdaaci5qo0tOVUAXOHAjDWyUnJ4tWOEU1eiW/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 Retry Executor
Open Agent mode in chat and ask: "Using Retry Executor, help me...". 3 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"retry-with-backoff-executor": {
"url": "https://edge.vinkius.com/vk_preview_SHWGIdaaci5qo0tOVUAXOHAjDWyUnJ4tWOEU1eiW/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 Retry Executor
Ask Copilot: "Using Retry Executor, help me...". 3 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"retry-with-backoff-executor": {
"url": "https://edge.vinkius.com/vk_preview_SHWGIdaaci5qo0tOVUAXOHAjDWyUnJ4tWOEU1eiW/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 Retry Executor
Open Cascade and ask: "Using Retry Executor, help me...". 3 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"retry-with-backoff-executor": {
"url": "https://edge.vinkius.com/vk_preview_SHWGIdaaci5qo0tOVUAXOHAjDWyUnJ4tWOEU1eiW/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 Retry Executor
Ask Cline: "Using Retry Executor, help me...". 3 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add retry-with-backoff-executor --transport http "https://edge.vinkius.com/vk_preview_SHWGIdaaci5qo0tOVUAXOHAjDWyUnJ4tWOEU1eiW/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 Retry Executor
Ask Claude: "Using Retry Executor, show me...". 3 tools are ready
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Bring your own AI
Change the model, client or framework. Keep Retry Executor connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
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Sourcegraph Cody -
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Amazon Q -
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Open WebUI -
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LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
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CrewAI -
Vercel AI SDK
Before you connect
Questions about Retry Executor.
The practical details behind the request, access and result.
How can I use Retry with Backoff Executor to stop my AI agents from crashing?
You can use this MCP to automatically manage delays after an error. Instead of the agent failing immediately, it uses calculated wait times to try again, making your workflows much more stable.
Will Retry with Backoff Executor help with LLM rate limits?
Yes. It is specifically designed to handle 429 errors by implementing exponential backoff, which spaces out your requests so you can stay within your provider's limits.
Can I use Retry with Backoff Executor with Claude or Cursor?
Yes, as long as your client is MCP-compatible, you can connect this to Claude, Cursor, Windsurf, or VS Code to add error recovery to your agent's capabilities.
Does this MCP prevent multiple agents from hitting an API at once?
Yes, it uses a technique called jitter. This adds a bit of randomness to the wait times so that multiple agents don't all retry at the exact same microsecond.
How do I know if my retry settings are actually working?
You can check the progress of your attempts to see exactly how many times a task has failed and how long the agent is waiting between tries.
How is the delay calculated?
The delay is calculated using the formula: base_delay * (backoff_factor ^ attempt_count), capped by the maximum delay. If jitter is enabled, a ±10% random variation is applied.
What is the purpose of jitter?
Jitter adds a small amount of randomness to the delay to prevent multiple clients from retrying at the exact same time, which helps avoid overwhelming the target service.
Can I use this to handle rate limits?
Yes, by including the specific rate limit error codes in the retryableErrorCodes list, the engine will automatically apply backoff to respect service limits.
One connection away
Give your agent a direct line to Retry Executor.
Connect Retry Executor once. Keep it beside 6,100+ managed Connectors when the next task needs more.
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