How to Use the Recurly MCP in LlamaIndex

Q: Can LlamaIndex agents modify Recurly subscriptions?

Yes, the agent can use createbillingaccount to register customers. While RAG is great for reading, the agent easily switches to writing when the user requests an action. You control which tools are exposed using the allowedtools filter.

Q: Does this Recurly integration support semantic search over invoices in LlamaIndex?

Your LlamaIndex agent uses listbillinginvoices to pull invoice metadata and converts it into searchable document nodes. This allows users to search their invoice history using natural language queries. The agent handles the retrieval and synthesis automatically.

Index live subscription data and run RAG queries over your Recurly accounts using LlamaIndex.

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Connect Recurly MCP to LlamaIndex

Create your Vinkius account to connect Recurly 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 Recurly with LlamaIndex

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Ground LlamaIndex RAG pipelines in Recurly data

The Recurly MCP Server connects your subscription data directly to your LlamaIndex knowledge agents. By exposing tools like `list_subscriptions` and `get_invoice`, the server lets you query billing states without manual data exports. Your agent pulls the raw API response and indexes it on the fly. This setup eliminates stale data issues in your customer support bots. When a user asks about their billing status, the agent queries the live API, indexes the text, and searches it semantically. You get accurate answers grounded in real-time facts.

Build searchable transaction indexes with this MCP Server

This MCP Server exposes `list_transactions` to let your agent build a searchable history of payments. LlamaIndex ingests these transaction logs directly into your vector store. Your agent can then answer complex questions about payment failures or refund histories instantly. You don't need to write custom ETL pipelines to sync your billing portal with your database. The agent acts as the bridge, fetching data with `get_subscription_details` and updating the index. It keeps your knowledge base fresh with zero manual maintenance.

Analyze subscription plans using LlamaIndex agents

The integration lets your agent call `list_subscription_plans` and `list_coupons` to help users choose the right tier. Your LlamaIndex agent compares active plans against user requirements pulled from conversational memory. It suggests the best fit based on actual, live pricing data. Because the tools return structured JSON, the indexer parses them without losing metadata. Your agent can filter plans by price, billing interval, or active discounts. It turns your static pricing page into an interactive, intelligent consultant.

Setup guide

Set up Recurly 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 Recurly 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 Recurly tools.",
)
response = await agent.run("List recent Recurly 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 Recurly. 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 Recurly MCP in LlamaIndex

You initialize the MCP tool specification with your Vinkius server URL and convert the tools to a list. Your LlamaIndex agent calls these tools to fetch raw subscription data, which it then indexes into your vector store. This keeps your search index updated with live billing details.

Yes, the agent can use `create_billing_account` to register customers. While RAG is great for reading, the agent easily switches to writing when the user requests an action. You control which tools are exposed using the allowed_tools filter.

Your LlamaIndex agent uses `list_billing_invoices` to pull invoice metadata and converts it into searchable document nodes. This allows users to search their invoice history using natural language queries. The agent handles the retrieval and synthesis automatically.

The server handles the raw API communication and returns standard error codes to the client. Your agent can catch these exceptions and retry the tool call. You can also configure rate-limiting policies at the Vinkius gateway level.

Vinkius executes the server in a secure, ephemeral V8 container. Customer billing accounts, subscription records, and payment tokens are processed in memory and never written to persistent storage. All traffic goes through encrypted TLS channels directly to the API endpoints.

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