How to Use the Blink Payment MCP in LlamaIndex

Q: How do I filter Blink Payment tools in LlamaIndex?

Use the allowedtools parameter in your agent setup. This limits the agent to only the functions you choose to expose.

Index your Blink Payment history into LlamaIndex for grounded, searchable financial knowledge.

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Connect Blink Payment MCP to LlamaIndex

Create your Vinkius account to connect Blink Payment 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 Blink Payment with LlamaIndex

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Query Blink Payment data with LlamaIndex

Transform your payment records into a searchable knowledge base. LlamaIndex takes output from `list_payments` and indexes it so your agents can answer questions about past transactions. This prevents the agent from guessing. It pulls exact IDs and amounts directly from your Blink Payment history.

Ground your LlamaIndex RAG in live data

Combine your internal documents with live Blink Payment API results. Your agent uses `get_customer` to fetch details, then cross-references them with your indexed data. This creates a unified view of your business. You get answers based on facts, not patterns.

Automate Blink Payment lookups via LlamaIndex

Use the `FunctionAgent` to execute `get_paylink` or `list_customers` on demand. The agent searches its index first, then calls the tool if it needs fresh information. This reduces unnecessary API calls. Your agent stays efficient by relying on indexed data before hitting the network.

Setup guide

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

It converts tool outputs into vectors. You can then run semantic searches over your transaction history to find specific customer details.

You don't need to train it. The agent queries the live API and indexes the results in real-time, keeping your knowledge base current.

Yes, once you hook up the tool spec, every payment record becomes a searchable entry. You can query your own history using natural language.

Use the allowed_tools parameter in your agent setup. This limits the agent to only the functions you choose to expose.

The server processes customer IDs and paylink details in a secure, ephemeral environment. Data is indexed only in your local vector store, keeping sensitive information off public models.

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