Agent Cost Allocation Engine Connector for AI agents.
3 live capabilities
Calculate precise workload costs and infrastructure chargebacks
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Why people use Agent Cost Allocation Engine
Agent Cost Allocation Engine for multi-agent financial visibility
With this MCP, that manual detective work disappears. You simply ask your agent to run a cost calculation, and it hands you a clean, itemized breakdown of every agent's spend. You get immediate, actionable data that turns a vague expense into a precise line item.
What Vinkius changes
You get a clear, mathematical breakdown of every cent your agents spend on infrastructure and compute.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 6,400+ Connectors
- Real-world use case 01
Internal Department Chargebacks
A FinOps lead needs to bill the Marketing department for their specific AI agents.
- Real-world use case 02
Detecting Rogue Agents
An engineer notices a sudden spike in the cloud bill.
- Real-world use case 03
Optimizing Agent ROI
A Product Manager wants to know if a new autonomous researcher is worth the cost.
Complete set · 3capabilities
The complete Agent Cost Allocation Engine capability set.
These are the exact actions your AI can choose when you ask it to work with Agent Cost Allocation Engine.
01—03
3 capabilities in this set.
Part of 3 available through Agent Cost Allocation Engine.
- 01 Capability
Calculate agent costs
Computes the specific cost for every agent in your system using your chosen allocation strategy. It makes individual agent billing precise and predictable.
- 02 Capability
Get resource efficiency summary
Provides a high-level overview of how your system performs against its total spend. Use this to see the big picture of your agentic ROI.
- 03 Capability
Analyze cost health
Evaluates your system to find financial risks or hidden inefficiencies. It helps you catch cost spikes before they break your budget.
Set up in minutes
One URL. Then ask Agent Cost Allocation Engine to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Agent Cost Allocation Engine 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_lx6FEjlfIUgQK1qt50mLpLvir9E9jNtsPXipGtGB/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 Agent Cost Allocation Engine, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Agent Cost Allocation Engine for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_lx6FEjlfIUgQK1qt50mLpLvir9E9jNtsPXipGtGB/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 Agent Cost Allocation Engine URL.
- Step 03
Save and start
Save the connection and enable Agent Cost Allocation Engine in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"agent-cost-allocation-engine": {
"url": "https://edge.vinkius.com/vk_preview_lx6FEjlfIUgQK1qt50mLpLvir9E9jNtsPXipGtGB/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 Agent Cost Allocation Engine
Open Agent mode in chat and ask: "Using Agent Cost Allocation Engine, help me...". 3 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"agent-cost-allocation-engine": {
"url": "https://edge.vinkius.com/vk_preview_lx6FEjlfIUgQK1qt50mLpLvir9E9jNtsPXipGtGB/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 Agent Cost Allocation Engine
Ask Copilot: "Using Agent Cost Allocation Engine, help me...". 3 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"agent-cost-allocation-engine": {
"url": "https://edge.vinkius.com/vk_preview_lx6FEjlfIUgQK1qt50mLpLvir9E9jNtsPXipGtGB/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 Agent Cost Allocation Engine
Open Cascade and ask: "Using Agent Cost Allocation Engine, help me...". 3 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"agent-cost-allocation-engine": {
"url": "https://edge.vinkius.com/vk_preview_lx6FEjlfIUgQK1qt50mLpLvir9E9jNtsPXipGtGB/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 Agent Cost Allocation Engine
Ask Cline: "Using Agent Cost Allocation Engine, help me...". 3 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add agent-cost-allocation-engine --transport http "https://edge.vinkius.com/vk_preview_lx6FEjlfIUgQK1qt50mLpLvir9E9jNtsPXipGtGB/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 Agent Cost Allocation Engine
Ask Claude: "Using Agent Cost Allocation Engine, show me...". 3 tools are ready
Where the request belongs
Work Agent Cost Allocation Engine can move forward.
This is built for the engineers and finance leads managing large-scale autonomous agent deployments who need to justify every dollar of compute spend.
AI Platform Engineer
Monitoring agentic workload costs to prevent budget overruns in production environments.
FinOps Analyst
Setting up accurate chargeback models to bill internal departments for their specific AI usage.
Product Manager
Evaluating the unit economics of new agentic features to ensure they are profitable.
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Bring your own AI
Change the model, client or framework. Keep Agent Cost Allocation Engine connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
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Before you connect
Questions about Agent Cost Allocation Engine.
The practical details behind the request, access and result.
How can the Agent Cost Allocation Engine help me manage my AI budget?
It provides a precise way to track exactly how much each agent is spending. Instead of seeing one large bill, you see a detailed breakdown of costs per agent and per task.
Can I use this MCP to bill different departments for their AI usage?
Yes. You can use the specific allocation capabilities to calculate exact chargebacks, making it easy to bill internal teams based on their actual agent activity.
How does this MCP handle shared costs like API or compute fees?
It allows you to distribute those shared expenses across your agents using different methods, such as a shared pool or activity-based logic, so no one agent carries the whole bill.
Will this help me find out which agents are too expensive?
Absolutely. You can use the efficiency and health capabilities to identify which specific agents are consuming too many resources for the work they are doing.
Can I connect this to my existing AI workflows?
Yes, you can connect this MCP to any compatible client like Claude, Cursor, or Windsurf to bring financial visibility directly into your development environment.
What allocation methods are supported?
The engine supports Direct Allocation, Shared Pool Allocation, and Activity-Based Allocation to distribute costs across agents.
How can I identify inefficient agents?
You can use analyze_cost_health to automatically flag optimization opportunities where agents have high costs but low success rates.
Does this include shared infrastructure costs?
Yes, by using the Shared Pool or Activity-Based methods in calculate_agent_costs, you can distribute costs from load balancers, databases, and monitoring to individual agents.
One connection away
Give your agent a direct line to Agent Cost Allocation Engine.
Connect Agent Cost Allocation Engine once. Keep it beside 6,400+ managed Connectors when the next task needs more.
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