# Resource Usage Accountant MCP for AI Agents AI Agent Connect > Resource Usage Accountant tracks and enforces resource consumption limits for agentic workflows. It monitors CPU time, memory, file descriptors, and network bytes to prevent runaway processes in multi-agent systems, ensuring your automated workflows stay within safe operational bounds. ## Overview - **Category:** monitoring - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_bznsDMWOuysMDjZ5IvPImPkxeg4PvkmLilVnVhkb/ai-agent-connect - **Tags:** resource-management, agent-governance, cost-control, monitoring, stability ## Description Running multiple AI agents at once is a great way to solve complex problems, but it can quickly spiral out of control. Without oversight, a single runaway agent can hog all your CPU, drain your memory, or flood your network, effectively crashing your entire orchestration session. This MCP acts as a digital accountant for your computational resources, keeping a strict eye on how much every agent consumes. You can set hard boundaries for specific agents or entire sessions, ensuring that no single task eats up more than its fair share of system power. It tracks everything from memory usage to network bytes and file descriptors. By integrating this into your workflow through the Vinkius catalog, you gain the ability to monitor real-time status and log usage increments as they happen. It's about moving from unpredictable, unbounded execution to a controlled, predictable environment where you know exactly what your automated workflows are costing you in terms of system resources. ## Tools ### get_usage_summary Get a summary of current resource usage and remaining limits for a scope ### apply_thresholds Update or set resource usage limits for a scope ### record_consumption Record incremental resource consumption for a scope ## Prompt Examples **Prompt:** ``` How much memory is the 'data_processor' agent using right now? ``` **Response:** ``` The **data_processor** agent is currently well within its limits: | Resource | Current Usage | Limit Remaining | | :--- | :--- | :--- | | **Memory** | 124 MB | 388 MB | | **CPU Time** | 450 ms | 9550 ms | | **Network** | 12 KB | 988 KB | It is currently operating at about 25% of its allocated memory capacity. ``` **Prompt:** ``` Set a strict 256MB memory limit for the 'web_crawler' agent. ``` **Response:** ``` I've updated the constraints for the **web_crawler** agent. **New Limits Applied:** * **Memory Limit:** 256 MB * **CPU Time Limit:** Unset * **File Descriptor Limit:** Unset * **Network Byte Limit:** Unset The agent will now be restricted to this memory ceiling. ``` **Prompt:** ``` Check if the current session is nearing its CPU limit. ``` **Response:** ``` I checked the status for the active session. You are approaching the threshold: * **Current CPU Usage:** 850ms * **Remaining CPU Budget:** 150ms * **Status:** **Warning: Approaching Limit** You might want to extend the limit or optimize the current task. ``` ## Capabilities ### Set resource boundaries Establish strict limits on memory, CPU, and network usage for specific agents or entire sessions. ### Monitor real-time consumption Check the current status of CPU, memory, and file descriptors to see how much headroom remains. ### Log usage increments Record specific measurements of resource consumption as agents perform their tasks. ### Prevent runaway processes Keep multi-agent workflows within safe operational bounds to avoid system crashes. ### Track network and file activity Keep an eye on network bytes and file descriptor counts to prevent resource exhaustion. ## Use Cases ### Preventing infinite loops in research agents An agent gets stuck in a recursive loop while searching for data. By using configure_limits, the system cuts the process before it eats all the available CPU. ### Managing memory for large-scale data processing A multi-agent team is processing massive datasets. You use get_current_status to ensure no single agent exceeds its 512MB memory limit. ### Controlling network traffic in web-scraping workflows A swarm of agents is crawling the web. You set limits on network bytes to prevent the agents from saturating your local bandwidth. ### Scaling multi-agent orchestration safely You spin up dozens of agents at once. Using report_usage, you track the cumulative footprint to ensure your server can handle the load. ## Benefits - Stop runaway agents from crashing your system by using configure_limits to set hard boundaries. - Keep a pulse on your infrastructure with get_current_status to see exactly how much memory is left. - Maintain precise logs of every agent's footprint by using report_usage to track consumption. - Prevent network congestion by monitoring and limiting network byte usage per session. - Avoid file descriptor exhaustion by setting limits on how many files an agent can open. - Ensure predictable costs and performance by enforcing strict CPU time limits. ## How It Works The bottom line is that you get a kill-switch and a dashboard for the computational costs of your AI workflows. 1. Define your resource thresholds for an agent or a session. 2. The agent reports its consumption metrics as it executes tasks. 3. You receive real-time status updates and enforcement of the set limits. ## Frequently Asked Questions **How can I stop an AI agent from using too much memory with Resource Usage Accountant?** You can use the configuration tools to set a specific memory ceiling for any agent. Once set, the system ensures the agent stays within that boundary, preventing it from consuming all available system RAM. **Can I monitor multiple agents at once using Resource Usage Accountant?** Yes. You can track resource consumption for individual agents or for an entire orchestration session, giving you a complete view of your total computational footprint. **Does Resource Usage Accountant work with any agentic framework?** It works with any agentic workflow that can interface with an MCP-compatible client. It is designed to be a flexible bridge for monitoring and enforcement. **How do I see the real-time status of my agent's CPU usage?** You can request a real-time status update at any time. This provides a clear breakdown of current CPU time, memory, and network usage compared to your set limits. **Will Resource Usage Accountant prevent my system from crashing?** It significantly reduces that risk. By enforcing limits on CPU, memory, and file descriptors, it prevents a single rogue agent from exhausting the resources your entire system needs to stay online. **How do I set resource limits for my agents?** You can use the `configure_limits` tool to define maximum allowed CPU time, memory, file descriptors, or network bytes for a specific agent or a whole session. **Can I track usage for an entire multi-agent session?** Yes, by providing a `sessionId` to `report_usage` or `get_current_status`, you can track the aggregate footprint of all agents participating in that session. **What happens when a resource limit is reached?** The `usageExceeded` flag in the status report will return true, allowing your orchestration logic to halt or adjust the agent's execution. **How do I check if an agent is approaching its limits?** You can use the `get_usage_summary` tool to retrieve the current usage and the remaining limits for a specific agent or session. **Can I set different limits for different agents?** Yes, you can use `apply_thresholds` to define custom maximum allowable values for CPU, memory, file descriptors, or network bytes for any specific scope. **When should I record resource consumption?** You should call `record_consumption` after every execution step to ensure the cumulative counters accurately reflect the agent's resource footprint.