Low Risk

count_tokens

Count tokens for a text string or chat messages array for any supported AI model (GPT-4o, Claude, Gemini, Mistral, Llama).

Part of the Ai Token Counter server.

count_tokens is read-only, but an agent in a loop can still rack up calls and cost. PolicyLayer caps every call before it runs. Live in minutes.

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AI agents call count_tokens to retrieve information from Ai Token Counter without modifying any data. This is common in research, monitoring, and reporting workflows where the agent needs context before taking action. Because read operations don't change state, they are generally safe to allow without restrictions -- but you may still want rate limits to control API costs.

Even though count_tokens only reads data, uncontrolled read access can leak sensitive information or rack up API costs. An agent caught in a retry loop could make thousands of calls per minute. A rate limit gives you a safety net without blocking legitimate use.

Read-only tools are safe to allow by default. No rate limit needed unless you want to control costs.

policy.json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "count_tokens": {}
  }
}

See the full Ai Token Counter policy for all 4 tools.

Get this rule live on your own Ai Token Counter server in minutes. PolicyLayer enforces it on every call, before it runs.

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These attack patterns abuse exactly the kind of access count_tokens gives an agent. Each links to the full case and the policy that stops it:

Browse the full MCP Attack Database →

Every attack above starts with a tool call. PolicyLayer checks each one against your policy first, so count_tokens only ever does what you allow.

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Other read tools across the catalogue. The same approach applies to each: allow, with a rate cap to control cost.

What does the count_tokens tool do? +

Count tokens for a text string or chat messages array for any supported AI model (GPT-4o, Claude, Gemini, Mistral, Llama).. It is categorised as a Read tool in the Ai Token Counter MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on count_tokens? +

Register the Ai Token Counter MCP server in PolicyLayer and add a rule for count_tokens: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Ai Token Counter. Nothing to install.

What risk level is count_tokens? +

count_tokens is a Read tool with low risk. Read-only tools are generally safe to allow by default.

Can I rate-limit count_tokens? +

Yes. Add a rate_limit block to the count_tokens rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.

How do I block count_tokens completely? +

Set action: deny in the PolicyLayer policy for count_tokens. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.

What MCP server provides count_tokens? +

count_tokens is provided by the Ai Token Counter MCP server (https://api.lazy-mac.com/ai-token-counter/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Ai Token Counter tool call.

Deterministic rules across all 4 Ai Token Counter tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.

Free to start. No card required.

4,600+ MCP servers and 31,000+ tools scanned and risk-classified.

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