# AI Token Counter MCP MCP > AI Token Counter lets you run raw text through a local calculation engine to get the exact token count for any given input. This is critical for building stable AI agents, especially when dealing with large documents or complex data structures. Instead of risking API crashes because your payload exceeds the context limit, this MCP gives you self-awareness over your data size, letting you chunk or summarize safely before making a single call to an LLM. ## Overview - **Category:** productivity - **Price:** Free - **Tags:** tokenization, context-window, llm-optimization, cost-management, api-limits, encoding ## Description When your agent has to process massive amounts of text—say, summarizing ten academic papers or reading a multi-gigabyte log file—it can't just send it all off to the API. If the data payload is too big, the connection fails and the whole pipeline dies. The problem is that LLMs themselves don't know how many tokens they are generating until *after* they generate them. This MCP solves that by letting your agent count tokens locally before anything else happens. You feed it raw text, and it spits out a precise number using industry-standard encoding. Knowing this exact figure lets you build safeguards into your workflow, deciding whether to chunk the data into smaller pieces or if the whole thing fits fine. If you're building complex AI workflows, connecting this through Vinkius’s catalog means you have reliable resource control right at the start of the process. ## Tools ### count_tokens Takes raw text input and returns the precise number of tokens it contains, preventing API overruns. ## Prompt Examples **Prompt:** ``` Tell me how many tokens are in this huge block of JSON data. ``` **Response:** ``` Token Count Result: There are 8540 tokens. ``` **Prompt:** ``` Count the tokens in this article so I know if I can fit it into my next prompt. ``` **Response:** ``` Token Count Result: The article has 2300 tokens. ``` **Prompt:** ``` I need the exact token count for this transcript snippet. ``` **Response:** ``` Token Count Result: Successfully counted. ``` ## Capabilities ### Measure input size The tool calculates exactly how many tokens are in any block of raw text data. ### Determine chunking needs You use the count to decide if a massive document must be split into smaller, manageable sections. ### Prevent API overload Your agent can check token limits locally, stopping potential fatal errors before they hit external services. ### Cost prediction Knowing the exact token count helps you accurately estimate your running costs per job run. ## Use Cases ### Summarizing a massive legal brief A paralegal uploaded a 50-page contract. Instead of blindly sending it to the agent, they call `count_tokens` first. If the count is too high, their agent automatically chunks the document by section and runs multiple smaller summary prompts instead. ### Processing multi-source research A data scientist has five different documents (PDF extracts) they need to summarize together. They run `count_tokens` on all five combined. If the total count is over the limit, the agent prompts the user to prioritize which sections to keep. ### Validating JSON payloads The backend developer needs to send a huge batch of structured data (JSON) for analysis. They use `count_tokens` on the raw JSON string to ensure it won't exceed the input limit, preventing runtime failures. ### Handling user-submitted text A customer submits a lengthy complaint or detailed support ticket. The agent uses `count_tokens` to verify the length immediately; if it’s too long for the main summary tool, it directs the user to an alternate, lower-capacity form. ## Benefits - System stability: Stop writing complex agents that randomly break when a document is just one token too long. Use this to validate payload size upfront. - Cost control: Calculate the exact resource cost for any job before running it, eliminating surprise API billing from over-sized prompts. - Reliable chunking: Determine if you need to split up massive inputs (like full books or large JSON files) into smaller pieces using `count_tokens` first. - Pre-validation logic: Build hard stops into your pipeline. If the text count exceeds a set limit, the agent can gracefully fail instead of crashing the entire system. - Accuracy: It uses the official encoding algorithm for exact counting, so you don't rely on rough estimates or word counts. ## How It Works The bottom line is that this MCP gives your agent reliable visibility into data size, so it never sends an oversized prompt again. 1. Feed the MCP a block of raw text, like a document or a JSON payload. 2. The tool runs an offline calculation using the standard encoding algorithm to count tokens. 3. You get back a precise number, telling you exactly how many API tokens the content uses. ## Frequently Asked Questions **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). **How does the `count_tokens` tool handle complex formats like JSON or code snippets?** It treats all inputs as raw text strings. You can pass structured data, and it accurately calculates tokens based on how an LLM tokenizes that entire block of content. **Does running `count_tokens` require connecting to an external API endpoint?** No, the calculation runs entirely locally within your agent's environment. This means your raw text never leaves your system while you count tokens, keeping everything private. **If I have several large documents, can `count_tokens` process them efficiently?** Yes. You pass the combined text from all documents to the tool. It returns a single, accurate total count for the entire payload quickly. **When building an RAG pipeline, what is the best workflow using `count_tokens`?** Run `count_tokens` immediately after fetching your documents. Use that resulting number to decide if you must chunk the data or if you can send everything at once. **What happens if my input text is too large for local processing?** The tool focuses on token counting, not memory management. If the text exceeds your client's available RAM, your agent will throw a standard resource error that you can then handle.