# Token Counter and Budget Enforcer MCP for AI Agents AI Agent Connect > Token Counter and Budget Enforcer MCP keeps your AI agents from running up massive bills. It provides precise token estimation for text and code, tracks cumulative usage across conversations, and enforces strict spending limits to prevent unexpected costs in multi-agent workflows. ## Overview - **Category:** utility - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_Fntor1z8zuXrJce6K2HdextT8uXupjEA6h8NsXAn/ai-agent-connect - **Tags:** tokens, budget, llm, usage-tracking, estimation ## Description Running autonomous agents can quickly turn into a financial headache if you aren't watching the token count. This MCP gives you a way to keep your agents on a leash. Instead of guessing how much a long prompt or a massive block of code will cost, you can get a precise estimate before you even hit send. It handles the math of tokenization so you don't have to. Beyond just estimating, it acts as a watchdog for your entire operation. It tracks exactly how much every agent and every conversation has consumed. You can set hard limits, and once an agent hits its budget, the enforcement mechanism kicks in to stop further spending. It's a vital layer of control for anyone building complex, multi-agent systems where one runaway loop could cost hundreds of dollars. By connecting this to your workflow through the Vinkius catalog, you gain a predictable way to manage LLM expenses without having to manually monitor every single request. ## Tools ### estimate_tokens Calculates the estimated token count for a provided text string. Use this to predict costs before sending large payloads. ### get_usage_summary Retrieves current cumulative totals without consuming new tokens. It's perfect for checking your current spend status. ### track_and_enforce_usage Updates the cumulative usage for an agent and a conversation, then checks against set limits. This is your primary budget guardrail. ## Prompt Examples **Prompt:** ``` How many tokens will this code snippet use? ``` **Response:** ``` The estimated token count for that code snippet is 4 tokens. ``` **Prompt:** ``` What is the current total usage for my research agent? ``` **Response:** ``` Your research agent has consumed a total of **1,250 tokens** so far. ``` **Prompt:** ``` Check if I've hit my limit for this conversation. ``` **Response:** ``` Current usage for this conversation is **450 tokens**. You are well within your set limit of 1,000 tokens. ``` ## Capabilities ### Predict text and code costs Get an accurate count of tokens for any string or code snippet before processing it. ### Monitor agent spending Keep a running tally of how many tokens each individual agent has used. ### Track conversation totals Watch the cumulative token consumption for specific chat sessions. ### Enforce hard budget limits Automatically stop usage once an agent or conversation hits a predefined threshold. ### Audit usage history Pull a summary of current totals to see exactly where your budget is going. ## Use Cases ### Preventing runaway agent loops An engineer sets a limit for a research agent. When the agent gets stuck in a loop, track_and_enforce_usage hits the limit and stops the process before the bill spikes. ### Pre-flight cost estimation A developer uses estimate_tokens to check a massive codebase before passing it to an agent, ensuring it fits within the expected context window and budget. ### Multi-tenant cost tracking A platform provider uses get_usage_summary to monitor how much each user's agent is consuming to ensure accurate billing. ### Budgeting for long-running tasks An automation specialist uses track_and_enforce_usage to ensure a background data-processing agent doesn't exceed its daily allocation. ## Benefits - Stop budget overruns by setting hard limits that your agent must respect. - Reduce uncertainty by using estimate_tokens to see costs before they happen. - Gain visibility into agent spending with get_usage_summary for clear auditing. - Prevent runaway loops in multi-agent systems using track_and_enforce_usage. - Simplify cost management by tracking usage per conversation automatically. ## How It Works The bottom line is you get a programmable safety net that prevents AI agents from exceeding your budget. 1. Connect the MCP to your preferred AI client via Vinkius. 2. Define your token limits for specific agents or conversation threads. 3. The agent uses the tools to check costs and log usage during every interaction. ## Frequently Asked Questions **How can I prevent my AI agents from spending too much money?** You can use the Token Counter and Budget Enforcer MCP to set strict limits. It tracks usage and can automatically stop an agent once it hits its budget. **Can I estimate the cost of a large file before sending it to an agent?** Yes, you can use the estimation tool to get a precise token count for any text or code before you process it, helping you avoid surprises. **How does the Token Counter and Budget Enforcer track usage?** It tracks cumulative token counts for both individual agents and specific conversation threads, allowing for granular budget control. **Is it possible to see a summary of my agent's total spend?** Yes, you can pull a summary at any time to see exactly how many tokens have been consumed without adding any new costs. **Will this work with my existing AI workflows?** Yes, as long as you are using an MCP-compatible client like Claude, Cursor, or Windsurf, you can connect this to manage your agent costs immediately. **How are tokens estimated?** Tokens are estimated using a deterministic character-to-token ratio: approximately 4 characters per token for English text and 3.5 characters per token for code blocks. **Can I set different limits for agents and conversations?** Yes. You can use `track_and_enforce_usage` to apply specific limits for both the unique agent identifier and the specific conversation identifier. **How do I check my current usage?** You can use the `get_usage_summary` tool to retrieve the cumulative token totals for a specific agent or conversation without consuming any new tokens.