# AI Token Counter MCP for AI Agents AI Agent Connect > AI Token Counter helps your AI agents see exactly how much space their prompts take up. It uses a local encoding standard to give you precise counts before you hit context limits. Stop worrying about truncated responses or crashed pipelines when your RAG system pulls in too much data. ## Overview - **Category:** productivity - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_3TlaUrJfXkjzPylHQtgw7qX3SzCKmj8umGecyFc1/ai-agent-connect - **Tags:** tokenization, context-window, llm-optimization, cost-management, api-limits, encoding ## Description This Connector gives your agent a way to measure the weight of the data first. When a RAG agent pulls a bunch of documents to summarize, it often blindly sends them to the external models. If that payload hits the limit, the system crashes and your pipeline breaks. These models can't see their own token counts before they send a prompt, so this tool fills that gap. It provides a way to measure data weight locally. This lets your agent do the math and chunk massive datasets safely before hitting external limits. Instead of hitting a wall and getting an error, your agent can now see that a file is too big and decide to summarize a smaller section or grab a different snippet. It turns a brittle process into a reliable one. By knowing the exact count before the request is made, you can build much more robust automation. You can find this tool in the Vinkius catalog to keep your automated workflows from hitting dead ends and ensure your agents always stay within the boundaries of their context windows. ## Tools ### count_tokens Send a block of text to get the exact count using the cl100k_base encoding. This lets you check if your data fits before you try to send it. ## Prompt Examples **Prompt:** ``` How many tokens are in this JSON data? ``` **Response:** ``` ### Token Count Result | Data Type | Token Count | Status | | :--- | :--- | :--- | | JSON Payload | **8,540** | ✅ Fits in context | Your data is well within the limits for most standard models. ``` **Prompt:** ``` Count the tokens in this article so I know if I can fit it. ``` **Response:** ``` ### Token Count Result * **Total Tokens:** 2,300 * **Status:** Ready to send You can fit this article into a standard context window with plenty of room left for your instructions. ``` **Prompt:** ``` I need the exact token count for this transcript snippet. ``` **Response:** ``` ### Token Count Result * **Count:** 8,540 tokens **Note:** This is a large snippet. You may want to summarize it in chunks to ensure the model captures all the details. ``` ## Capabilities ### Measure prompt size Get the exact count for any block of text to see how much space it occupies. ### Prevent context crashes Stop your agent from sending payloads that are too large for the model to handle. ### Smart chunking Let your agent decide how to split up data based on real token counts rather than character limits. ### Cost tracking Know the exact size of your payload before you send it to stay within your budget. ### Local calculation Count tokens on your machine without making extra calls to an external provider. ## Use Cases ### RAG Document Summarization An agent pulls 15 documents. It uses count_tokens to see they're too big, so it summarizes 5 at a time to stay under the limit. ### JSON Data Processing You have a massive JSON file. The agent checks the token count first to decide if it needs to split the file into smaller pieces. ### Transcript Analysis You're processing a 2-hour meeting. The agent counts the tokens and creates a series of smaller summaries to stay under the limit. ### Cost Optimization A developer wants to keep every request under 2,000 tokens. The agent checks the count and trims the prompt if it's too long. ## Benefits - Stop crashes: Use count_tokens to ensure your data never exceeds the model's limit, keeping your pipelines running smoothly. - Accurate math: Get the exact counts using the cl100k_base standard so you aren't guessing about how much space you have left. - Better chunking: Let your agent intelligently split data into smaller pieces based on real token counts rather than just character limits. - Cost control: Know the exact size of your payload before you send it, making it much easier to stay within your budget. - Local speed: The tool runs locally, so you don't have to waste time or money on extra calls just to check a count. ## How It Works The bottom line is you get a way to stop your AI from guessing about context limits. 1. Provide the raw text or data to your agent. 2. The agent calls the tool to get the exact token count. 3. You get a number you can use to decide whether to proceed or split the data. ## Frequently Asked Questions **What does the AI Token Counter MCP do?** It gives your AI agent the ability to see exactly how many tokens are in a piece of text before it sends it to a model. This helps prevent errors caused by exceeding the model's limits. **How does this help with RAG systems?** It prevents your RAG pipeline from crashing when it retrieves too much data. Your agent can check the size of the documents it found and decide how to summarize them safely. **Does the AI Token Counter work for both OpenAI and Claude?** Yes, it uses the cl100k_base encoding standard, which is the same one used by major models like GPT-4 and Claude. **Why can't I just count the words instead?** Models don't see words; they see tokens. A word can be one token or several. Using this Connector ensures you get the exact number the model will see, not just a word count. **How does this save me money?** By knowing the exact token count before you send a request, your agent can trim unnecessary data, ensuring you don't pay for more tokens than you actually need. **Does it work offline?** Yes, the token counting happens locally on your machine. You don't need to make any extra API calls to get the count. **How do I stop my agent from crashing?** Connect this Connector to your agent. It will then be able to check the size of its own data and automatically handle chunks that are too large for the context window. **What tokenizer algorithm is used?** It uses the `cl100k_base` encoding, which is the exact algorithm used by GPT-3.5, GPT-4, and most Claude models. **Does it send my text to OpenAI?** No. The calculation happens 100% local within the Edge engine using mathematical mapping. **Is it safe for large texts?** Yes, it evaluates the exact token structure rapidly. But keep in mind standard Edge memory limits (under 10MB per payload).