- STABLE
- CLEAN
- PRECISE
- HUMAN
Short answer
How do I stop my AI from hitting context limits?
Your agent uses this MCP to calculate the exact token count of a payload before it hits the API. This allows the agent to trim or split data to stay within limits. You get a way to prevent crashes and lost work.
Where every outcome lands.
The results of token counting
This is what happens once your agent starts measuring data.
STABLE
No more crashes
The agent identifies if a payload is too large. You get a stable connection that doesn't drop mid-request.
CLEAN
Trimmed payloads
The agent sees the exact token count. You get a way to prune unnecessary text to fit the window.
PRECISE
Exact measurements
The agent uses the tool to get a real count. You get data that matches what the API actually sees.
HUMAN
Manual decisions
The agent flags when a limit is near. You decide whether to delete parts of the text or split the task.
The workflow
What your AI does when a payload arrives.
The agent uses the tool to validate data size before any API calls are made.
Measure size
The agent takes your text or file and calculates the token count. This provides the raw number needed for the next step.
count_tokensCheck limits
The agent compares the count against the target API's context window. This determines if the payload is safe to send.
count_tokensFlag errors
If the count is too high, the agent stops the process. It prevents the API from returning a context error.
count_tokensPropose fixes
The agent suggests ways to reduce the count. You get options to shorten the text or summarize it.
count_tokens
Try it
Copy these to start.
Use these prompts to begin managing your context window.
Starting points
These are starting lines. Swap the bracketed text for your actual files or messages.
GPT Tokenizer Connector
You're all set. Choose your MCP client and follow the setup instructions.
https://edge.vinkius.com/vk_preview_3TlaUrJfXkjzPylHQtgw7qX3SzCKmj8umGecyFc1/mcpClaude 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.
{
"mcpServers": {
"ai-token-counter-mcp": {
"url": "https://edge.vinkius.com/vk_preview_3TlaUrJfXkjzPylHQtgw7qX3SzCKmj8umGecyFc1/mcp"
}
}
}Count the tokens in this long email draft so I know if it fits.
Check the token size of the contents in this GitHub repo.
How many tokens is the text in this Google Doc?
Before you send that response, count the tokens to make sure we don't hit the limit.
Claude
ChatGPT
Cursor
VS Code
Windsurf
Claude Code
JetBrains
Cline
Start here
Connect AI Token Counter once, then ask.
Click the link to connect the MCP to your client. Your credentials stay encrypted, and you can start asking questions immediately.
Connect AI Token Counter to your AIFAQ
How this task behaves
- 01
Can the AI automatically delete text to fit the limit?
No. The agent can suggest what to remove, but you must decide what to delete.
- 02
Does this tool change my original files?
No. The tool only reads the data to count tokens; it never modifies your files or messages.
- 03
Will this prevent all AI errors?
No. It only prevents errors caused by exceeding the context window. It won't stop logic errors or API downtime.
- 04
How accurate is the token count?
The agent uses the specific tool to get the exact count used by the underlying logic.
- 05
Can the agent send the payload for me?
No. This MCP only counts tokens. Your agent still handles the actual sending of the data.
More questions about AI Token Counter? The Connector page answers them. See everything the AI Token Counter Connector can do
Connect GPT Tokenizer to Claude, Cursor, ChatGPT & more
