How to Use the Sharpei MCP in LlamaIndex

Q: How does Sharpei help LlamaIndex with recurring payments?

LlamaIndex indexes the output of getsubscription, turning structured payment data into semantic search knowledge. You can then query past billing cycles or future renewal dates accurately.

Q: Can I use Sharpei to build a comprehensive history with LlamaIndex?

Yep. By indexing results from listapplications and getdocuments, you create a deep, unified knowledge base covering the entire application lifecycle for your users.

Q: What if I need verification status info for LlamaIndex?

You use getverificationstatus to pull the current data point. Indexing this keeps the search results fresh, ensuring your AI agent always has the most up-to-date facts.

Index e-commerce knowledge: Sharpei for LlamaIndex RAG systems.

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Connect Sharpei MCP to LlamaIndex

Create your Vinkius account to connect Sharpei to LlamaIndex — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Sharpei with LlamaIndex

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Claude

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Building Knowledge Bases with MCP Server

`get_document` pulls specific document details, which you can index into your vector store. This means if a user asks about an old payment policy, the answer is grounded in real API data. LlamaIndex then lets you query this live knowledge base, combining historical documents with current operational facts.

Querying Subscriptions with LlamaIndex

`list_subscriptions` provides a list of all active recurring plans. Indexing these results allows users to ask questions like, 'Which customers pay annually?' and get an answer based on the actual API data. The RAG application doesn't hallucinate; it points straight back to the relevant subscription record.

Tracking App Lifecycle via LlamaIndex

`list_applications` pulls all current verification applications. By indexing these, you create a searchable log of every client interaction. This lets your AI agent answer complex questions like, 'What documents were associated with the application opened last Tuesday?'

Setup guide

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

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Common questions about Sharpei MCP in LlamaIndex

LlamaIndex indexes the output of `get_subscription`, turning structured payment data into semantic search knowledge. You can then query past billing cycles or future renewal dates accurately.

Yep. By indexing results from `list_applications` and `get_documents`, you create a deep, unified knowledge base covering the entire application lifecycle for your users.

You use `get_verification_status` to pull the current data point. Indexing this keeps the search results fresh, ensuring your AI agent always has the most up-to-date facts.

It's solid. Because it provides specific data points through tools like `get_application` and `list_webhooks`, you can combine these discrete outputs into a single, powerful index.

This server handles document records. When indexing this information, ensure that any personally identifiable information within the documents remains secure and is only used for retrieval.

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