How to Use the AI Token Counter MCP in LlamaIndex

Q: How do I integrate the AI Token Counter with my LlamaIndex RAG setup?

Install llama-index-tools-mcp, then create a McpToolSpec pointing to your Vinkius endpoint. This spec exposes the token counting tool, which your query engine or agent can then call to manage context from retrieved nodes.

Build RAG apps on LlamaIndex that never break context. This MCP accurately counts tokens before you index or query.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect AI Token Counter MCP to LlamaIndex

Create your Vinkius account to connect AI Token Counter to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup AI Token Counter with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Smarter Document Indexing

This MCP gives your LlamaIndex ingestion pipeline a critical control gate. As you process documents for a vector store, you can use the `count_tokens` tool to measure each chunk's size. This ensures your chunks are optimized for your embedding model and final query context. Stop guessing at chunk sizes or using character counts as a proxy. Get the exact token count and build a more effective, densely packed index. Your RAG queries return better results because the source material was indexed correctly from the start.

Context-Aware Query Engines

A LlamaIndex query engine often pulls multiple nodes from an index to answer a question. The problem is, you don't know if the combined context will fit into your LLM's prompt window until it's too late. This tool solves that. Your query engine can now retrieve nodes, use this MCP to count the total token size, and then decide to drop, summarize, or refine the nodes before building the final prompt. It turns a reactive process into a proactive, intelligent one.

Ground Your Agent with an MCP Server

When you give a LlamaIndex agent access to tools, you want it to make smart decisions. This MCP server provides a fundamental piece of metadata: token count. The agent can now reason about its own operational limits. Instead of just fetching data, the agent can ask, "How big is this data?" before it proceeds. This simple check makes the agent more robust and prevents it from blindly walking into API errors.

Setup guide

Set up AI Token Counter MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all AI Token Counter MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to AI Token Counter tools.",
)
response = await agent.run("List recent AI Token Counter data")

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by GPT Tokenizer. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect AI Token Counter now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about AI Token Counter MCP in LlamaIndex

Install `llama-index-tools-mcp`, then create a `McpToolSpec` pointing to your Vinkius endpoint. This spec exposes the token counting tool, which your query engine or agent can then call to manage context from retrieved nodes.

Yes. By counting tokens before sending a payload to a commercial LLM, your agent can make cost-aware decisions. It can choose to summarize text or use a cheaper model if the token count exceeds a certain budget.

The tool is completely independent of your vector store. It operates on the text content after it has been retrieved, but before it's sent to the LLM. It works with any backend you use.

It's a perfect fit. A FunctionAgent in LlamaIndex needs tools to interact with the world. Providing it with the token counter gives it self-awareness about the size of data it's handling, preventing context overflow errors.

The MCP only ever sees the raw text string you submit for counting. Your data passes through Vinkius's ephemeral infrastructure, is processed in memory inside a V8 isolate, and is never written to disk.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript