How to Use the Invoiced MCP in LlamaIndex

Q: How do I index Invoiced data using LlamaIndex?

You install llama-index-tools-mcp and initialize the BasicMCPClient with your Vinkius URL. From there, you convert the Invoiced tools into an McpToolSpec and load them as tools for your LlamaIndex query engine.

Q: Can LlamaIndex search across multiple Invoiced customers at once?

Yes, you can use listcustomers to fetch your entire Invoiced client roster and index the results. This allows your LlamaIndex agent to run semantic searches across all customer profiles simultaneously.

Q: Does LlamaIndex support filtering specific Invoiced tools?

Yes, you can use the allowedtools filter when configuring your LlamaIndex tool spec to restrict access. This ensures your agent only calls listplans or listtaxrates when handling Invoiced subscription queries.

Index live Invoiced customer payment histories and subscription details into LlamaIndex vector stores for semantic search.

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Connect Invoiced MCP to LlamaIndex

Create your Vinkius account to connect Invoiced 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 Invoiced with LlamaIndex

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Index Invoiced subscriptions for LlamaIndex RAG audits

The `list_subscriptions` tool pulls live recurring revenue data from Invoiced directly into your LlamaIndex pipelines. It allows you to convert raw Invoiced subscription histories into document nodes that are ready for LlamaIndex semantic indexing. Once indexed, your LlamaIndex agent can query past Invoiced subscription states without hitting the API repeatedly. This reduces latency and keeps your billing answers grounded in real-time Invoiced contract statuses.

Search invoice histories using vector embeddings

The `list_invoices` tool retrieves historical Invoiced records so LlamaIndex can embed them into a searchable vector database. This MCP tool allows your LlamaIndex agent to perform natural language queries over complex payment records instead of writing SQL filters. Your LlamaIndex agent can quickly match customer disputes against historical Invoiced records by querying the localized index. This architecture prevents hallucinations by forcing the LLM to retrieve actual Invoiced records before answering.

Build knowledge graphs from customer balances

The `get_customer` tool fetches detailed balance sheets and contact information from Invoiced to enrich your LlamaIndex knowledge graphs. This MCP tool feeds structural customer metadata into your LlamaIndex index to connect billing entities with their historical transactions. By linking these nodes with `list_payments`, you build a complete visual map of Invoiced account health inside LlamaIndex. Your support agent can query this graph to trace Invoiced payment patterns and spot delinquent accounts instantly.

Setup guide

Set up Invoiced 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 Invoiced 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 Invoiced tools.",
)
response = await agent.run("List recent Invoiced data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Invoiced. 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.

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Common questions about Invoiced MCP in LlamaIndex

You install `llama-index-tools-mcp` and initialize the `BasicMCPClient` with your Vinkius URL. From there, you convert the Invoiced tools into an `McpToolSpec` and load them as tools for your LlamaIndex query engine.

Yes, you can use `list_customers` to fetch your entire Invoiced client roster and index the results. This allows your LlamaIndex agent to run semantic searches across all customer profiles simultaneously.

The server forces your LlamaIndex agent to retrieve actual records using tools like `get_invoice` before formulating a response. Because LlamaIndex embeds these real Invoiced JSON payloads into the context window, the model cannot invent fake balances.

Yes, you can use the `allowed_tools` filter when configuring your LlamaIndex tool spec to restrict access. This ensures your agent only calls `list_plans` or `list_tax_rates` when handling Invoiced subscription queries.

All Invoiced billing and payment data fetched by this MCP server is processed in an ephemeral V8 sandbox. Vinkius secures your credentials, meaning your raw Invoiced API keys are never exposed to the LLM or stored in your LlamaIndex vector database.

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