How to Use the Togai (Usage Metering & Pricing) MCP in LlamaIndex

Q: Does LlamaIndex handle large volumes of usage data?

Yes. By indexing the output from ingestbatchevents, you can manage massive amounts of event data and still query it semantically later on, making historical analysis simple.

Q: Can I use the MCP Server for both current and historical billing data in LlamaIndex?

Absolutely. You can index results from listinvoices (history) alongside real-time checks like checkentitlement, giving your application a full view of operational status.

Turn billing data into searchable knowledge with LlamaIndex and MCP Server.

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Connect Togai (Usage Metering & Pricing) MCP to LlamaIndex

Create your Vinkius account to connect Togai (Usage Metering & Pricing) 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.

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Setup Togai (Usage Metering & Pricing) with LlamaIndex

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Search Financial Rules with the MCP Server

LlamaIndex indexes results from `list_rate_cards` and `list_accounts`, making them part of your knowledge base. Instead of running a specific query, you can ask questions like: 'What is the current rate card for Account X?' The system grounds its answers in actual data—like fetching metrics via `get_metrics` or checking existing pricing plans using `create_price_plan`.

Query Past Usage and Billing Events

The tool output can be indexed to answer questions about past usage. For example, you query the system to understand why a customer was charged based on historical events logged by `ingest_event`. This is better than relying on simple API calls. It also allows retrieval of old invoices using `list_invoices`, creating an accurate record that isn't limited to current data.

Build RAG Applications for Account Management

You can build a complete knowledge graph around customer profiles. By indexing the results of `get_customer` and `update_customer`, your application answers complex queries like: 'Show me all accounts that need an entitlement check because their usage is high.' This turns transactional API calls into semantic, queryable information.

Setup guide

Set up Togai (Usage Metering & Pricing) 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 Togai (Usage Metering & Pricing) 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 Togai (Usage Metering & Pricing) tools.",
)
response = await agent.run("List recent Togai (Usage Metering & Pricing) data")

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Common questions about Togai (Usage Metering & Pricing) MCP in LlamaIndex

LlamaIndex indexes the results of billing tools. You can query past usage data, account details (`get_account`), and even pricing rules to answer complex questions without needing to write specific code for every change.

Yes. By indexing the output from `ingest_batch_events`, you can manage massive amounts of event data and still query it semantically later on, making historical analysis simple.

The server touches account details, rate cards, entitlements, and wallet balances. When indexing, you are working with identifiers like `accountId`, billing schemas, and customer records.

Absolutely. You can index results from `list_invoices` (history) alongside real-time checks like `check_entitlement`, giving your application a full view of operational status.

The server touches account details, usage events, and billing records. When setting up the index, you control which specific fields from these data types are exposed for querying.

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