Does this count the tokens for all LLMs?

No, it uses the specific cl100k_base encoding algorithm. This ensures the count matches the tokenization method used by the target LLM API, making the count accurate for your workflow.

Is this better than just guessing the token count?

Yes. Guessing is unreliable. This MCP provides a precise, local count of the raw text, giving your agent the mathematical certainty it needs to avoid unexpected API failures.

Can my agent use this to save money?

Absolutely. By knowing the exact token count, your agent can optimize its prompts and chunking strategy, preventing unnecessary API calls and keeping your costs down.

What kind of data can I feed into the token counter?

You can feed it any raw text, including JSON data, articles, transcripts, or any large block of text your agent needs to analyze.

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).

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Connect AI Token Counter to ChatGPT, Claude and Gemini

Set it up once and it works in ChatGPT, Claude, Cursor, and any other client you love. It all runs safely in the background, so you can relax and focus on what really matters. Keep your complex workflows running without hitting API limits.

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

Your AI does the work. We keep it safe and sound.

No other AI tool gives you this. Your workflow runs in the client you already love, your account stays protected, and you decide what the agent can and cannot do. It is the whole reason to make the switch.

Each Connector lives inside our infrastructure, the same place where your workflow runs. The three things below are already part of it, working quietly in the background so you never have to think about them. Nothing to add, nothing to set up, nothing to worry about.

V8 Isolate01

Each Connector runs inside its own sealed environment.

Each Connector runs inside its own sealed V8 runtime — no access to your files, your system, or other servers. A Connector never sees past its own request.

Kill Switch02

One switch stops every agent. Instantly.

One action deactivates every Connector, revokes every token, and ends every session at once. AI Governance keeps the record.

FinOps per User03

Spending limits that pause instead of surprise.

Every user keeps their own connections and credentials, isolated from everyone else. Anything that would cross your spending limit is held for your approval, not billed.

Three controls, built into the layer your AI connects through.

Curated set · 1 capability

We ship only the actions that matter.

Capability set01 / 01

01

1 capability in this set.

Part of 1 available through GPT Tokenizer.

01
Count tokens
Pass the raw text and receive the exact token count. Use the result to decide whether to chunk, summarize, or send directly. Counts exact LLM tokens (cl100k_base) offline. Prevents RAG agents from exceeding context windows and crashing

Real work

What teams actually run

Summarizing Legal Documents

Instead of sending a 100-page contract at once, your agent counts the tokens, breaks the contract into chapters, and summarizes each section individually.

Building RAG Pipelines

Your agent fetches 50 documents. It uses this MCP to count the total tokens, ensuring the final query and context payload stays under the API limit.

Analyzing Transcripts

For long meeting transcripts, the agent counts the tokens to determine if it needs to chunk the data or if it can summarize the whole thing.

Batch Data Processing

When processing thousands of records, the agent counts the token load per batch to manage API calls efficiently and avoid throttling.

Automate a task

Put GPT Tokenizer to work on one thing.

Step-by-step automations your AI runs through this Connector: the workflow, the prompts to start with, and the exact capabilities behind each move.

One connector, every AI

GPT Tokenizer works with the most popular AI clients.

These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.

Building your own app? The connector is yours to use.

You don't need a client to put GPT Tokenizer to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.

Observed, not estimated

1881ms average. Fast in production.

GPT Tokenizer is checked daily against the live service.

Daily averagePeak 2212ms

Oct 3Today

Fastest day: 1634ms
Slowest day: 2212ms
14-day trend: Slowing+9%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 1 capability arrives ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of GPT Tokenizer, so you can see the experience inside your AI.

It does not authenticate your account with GPT Tokenizer. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

GPT Tokenizer Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_3TlaUrJfXkjzPylHQtgw7qX3SzCKmj8umGecyFc1/mcp

Claude Desktop

Follow the steps below to connect in seconds.

1In Claude Desktop, open Settings → Connectors.
2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
3Click Add and start a new chat — GPT Tokenizer capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "ai-token-counter-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_3TlaUrJfXkjzPylHQtgw7qX3SzCKmj8umGecyFc1/mcp"
    }
  }
}

Claude
ChatGPT
Cursor
VS Code
Windsurf
Claude Code
JetBrains
Cline

Step-by-step instructions for each client are in the guide. How to connect

Guided setup for Claude? Set up GPT Tokenizer in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work GPT Tokenizer owners hand off.

This MCP is built for developers and prompt engineers who build complex, multi-step AI agents. If your workflow involves summarizing large documents, processing extensive data sets, or building Retrieval Augmented Generation (RAG) pipelines, you need this capability. It gives your agent the necessary math to handle real-world data limits.

01
AI Developer
Builds agents that must reliably process inputs of unknown size, preventing runtime API failures.
02
Prompt Engineer
Designs prompts that handle massive amounts of context, ensuring the prompt doesn't exceed the model's limit.
03
Data Scientist
Needs to pre-process and chunk large data sources before feeding them into an LLM for analysis.

FAQ

Questions GPT Tokenizer owners ask.

01
Does this count the tokens for all LLMs?
No, it uses the specific cl100k_base encoding algorithm. This ensures the count matches the tokenization method used by the target LLM API, making the count accurate for your workflow.
02
Is this better than just guessing the token count?
Yes. Guessing is unreliable. This MCP provides a precise, local count of the raw text, giving your agent the mathematical certainty it needs to avoid unexpected API failures.
03
Can my agent use this to save money?
Absolutely. By knowing the exact token count, your agent can optimize its prompts and chunking strategy, preventing unnecessary API calls and keeping your costs down.
04
What kind of data can I feed into the token counter?
You can feed it any raw text, including JSON data, articles, transcripts, or any large block of text your agent needs to analyze.
05
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.
06
Does it send my text to OpenAI?
No. The calculation happens 100% local within the Edge engine using mathematical mapping.
07
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).

Explore

More in Productivity

Connect GPT Tokenizer to Claude, Cursor, ChatGPT & more

Your AI does the work. We keep it safe and sound.

Each Connector runs inside its own sealed environment.

One switch stops every agent. Instantly.

Spending limits that pause instead of surprise.

We ship only the actions that matter.

01

Count tokens

What teams actually run

Summarizing Legal Documents

Building RAG Pipelines

Analyzing Transcripts

Batch Data Processing

Building your own app? The connector is yours to use.

GPT Tokenizer Connector

Claude Desktop

AI Developer

Prompt Engineer

Data Scientist

Does this count the tokens for all LLMs?

Is this better than just guessing the token count?

Can my agent use this to save money?

What kind of data can I feed into the token counter?

What tokenizer algorithm is used?

Does it send my text to OpenAI?

Is it safe for large texts?

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