Batch Request Optimizer Connector for AI agents.
3 live capabilities
Reduce LLM API costs and latency through smart request batching
Waiting for input…
Why people use Batch Request Optimizer
Stop wasting LLM budget with Batch Request Optimizer
This MCP changes the math. Instead of that repetitive loop, you hand your requests over to a system that organizes them into optimized groups. You stop worrying about individual API limits and start looking at your workload as a single, efficient stream. You get faster results and a much lower bill, all while having the confidence that your batches won't crash halfway through.
What Vinkius changes
You turn expensive, slow individual API calls into efficient, high-throughput batch operations.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 6,400+ Connectors
- Real-world use case 01
Massive dataset labeling
An engineer needs to label 10,000 rows of data.
- Real-world use case 02
Preventing production timeouts
A developer is worried about large batches failing.
- Real-world use case 03
Budget tracking for research
A researcher wants to know if their new batching strategy is actually working.
Complete set · 3capabilities
The complete Batch Request Optimizer capability set.
These are the exact actions your AI can choose when you ask it to work with Batch Request Optimizer.
01—03
3 capabilities in this set.
Part of 3 available through Batch Request Optimizer.
- 01 Capability
Assess batch risk
Checks your batching plan for potential failures. It flags batches that are too large and might trigger timeouts.
- 02 Capability
Calculate batch plan
Creates the actual grouping of requests. You can choose between fixed, dynamic, or priority-based strategies.
- 03 Capability
Analyze batch efficiency
Provides a breakdown of your savings. It calculates how much you've reduced latency and token costs.
Set up in minutes
One URL. Then ask Batch Request Optimizer to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Batch Request Optimizer 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_hEQEvIT6CmbMrr99NTgzxWSJ3UgwdHfw3sW6cDe1/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 Batch Request Optimizer, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Batch Request Optimizer for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_hEQEvIT6CmbMrr99NTgzxWSJ3UgwdHfw3sW6cDe1/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 Batch Request Optimizer URL.
- Step 03
Save and start
Save the connection and enable Batch Request Optimizer in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"batch-request-optimizer": {
"url": "https://edge.vinkius.com/vk_preview_hEQEvIT6CmbMrr99NTgzxWSJ3UgwdHfw3sW6cDe1/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 Batch Request Optimizer
Open Agent mode in chat and ask: "Using Batch Request Optimizer, help me...". 3 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"batch-request-optimizer": {
"url": "https://edge.vinkius.com/vk_preview_hEQEvIT6CmbMrr99NTgzxWSJ3UgwdHfw3sW6cDe1/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 Batch Request Optimizer
Ask Copilot: "Using Batch Request Optimizer, help me...". 3 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"batch-request-optimizer": {
"url": "https://edge.vinkius.com/vk_preview_hEQEvIT6CmbMrr99NTgzxWSJ3UgwdHfw3sW6cDe1/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 Batch Request Optimizer
Open Cascade and ask: "Using Batch Request Optimizer, help me...". 3 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"batch-request-optimizer": {
"url": "https://edge.vinkius.com/vk_preview_hEQEvIT6CmbMrr99NTgzxWSJ3UgwdHfw3sW6cDe1/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 Batch Request Optimizer
Ask Cline: "Using Batch Request Optimizer, help me...". 3 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add batch-request-optimizer --transport http "https://edge.vinkius.com/vk_preview_hEQEvIT6CmbMrr99NTgzxWSJ3UgwdHfw3sW6cDe1/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 Batch Request Optimizer
Ask Claude: "Using Batch Request Optimizer, show me...". 3 tools are ready
Where the request belongs
Work Batch Request Optimizer can move forward.
This is for engineers and researchers running high-volume LLM workloads who are tired of watching their API budgets disappear or waiting hours for batch processing to finish.
LLM Engineer
Optimizing inference costs and latency for production-grade agentic workflows.
Data Scientist
Processing massive datasets through LLMs without hitting rate limits or blowing the budget.
AI Ops Engineer
Managing the reliability and cost-efficiency of large-scale model deployments.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse Connectors
Agent Timeout & Cascading Delay Calculator
Calculate deterministic timeout allocations and predict cascading delays in multi-agent workflows.
Embedding Dimension Optimizer
A deterministic capability to balance embedding quality, latency, and storage efficiency.
Tool Call Optimizer
A deterministic calculator to optimize MCP capability token footprints.
Agent Tool Selection Router
A deterministic engine for selecting and sequencing optimal capabilities for AI agents.
Tool Argument Completeness Checker
Audits LLM capability calls to detect missing parameters and value hallucinations.
Kubernetes Resource Request Calculator
Computes Kubernetes CPU/memory requests and limits from observed usage metrics (p50/p95/p99).
Bring your own AI
Change the model, client or framework. Keep Batch Request Optimizer connected.
-
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 Batch Request Optimizer.
The practical details behind the request, access and result.
How can the Batch Request Optimizer reduce my LLM costs?
It groups multiple requests together, which reduces the redundant token overhead sent with every call, directly lowering your total API spend.
Can I use Batch Request Optimizer with Claude or Cursor?
Yes. You can connect this MCP to any compatible client like Claude, Cursor, or Windsurf to start optimizing your requests immediately.
Will batching my requests make my agent slower?
Actually, it usually makes things faster. By reducing the number of individual network round-trips, you often see a significant drop in total latency.
How does Batch Request Optimizer handle API rate limits?
It organizes your requests into structured batches, allowing you to stay within your provider's limits by controlling how many requests are sent at once.
Is it safe to send very large batches of requests?
You shouldn't guess. You can use the risk assessment capability to check if a batch is too large and might trigger a timeout before you actually run it.
What kind of batching strategies are available?
You can choose from fixed batch sizes, dynamic sizing based on your needs, or priority-based grouping to ensure important tasks go first.
One connection away
Give your agent a direct line to Batch Request Optimizer.
Connect Batch Request Optimizer once. Keep it beside 6,400+ managed Connectors when the next task needs more.
Explore every Connector No credit card required · Free tier available