How to Use the SEON MCP in LlamaIndex

Q: How does LlamaIndex integrate SEON fraud data?

LlamaIndex indexes the output from tools like checkfraud. Instead of just passing a score, it stores the reasoning and the raw results, allowing you to query the why behind the risk score later.

Q: Can I query historical transaction data using LlamaIndex and SEON?

Yes. By indexing the results of gettransaction, you can query past activity and get answers grounded in actual API data, which is perfect for compliance checks.

Q: Does LlamaIndex support complex rule analysis with SEON?

It does. You can index the outputs from listrules alongside procedural documents. This lets you query and understand exactly how different fraud rules apply across various scenarios.

Index SEON's security data into your knowledge base with LlamaIndex.

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Connect SEON MCP to LlamaIndex

Create your Vinkius account to connect SEON 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 SEON with LlamaIndex

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LlamaIndex: Indexing Fraud Data

MCP tool output becomes searchable knowledge. Run `check_fraud` or `get_transaction`, and the results get indexed into your vector store. Later, you query this index—you're asking questions about past fraud scores, not just running live checks. This means your AI app can answer things like, 'What was the risk score for user X last month?' based on actual SEON data.

MCP Server: Building Contextual Security Knowledge

You build a RAG application that combines live API calls and stored documents. For instance, you can index compliance reports alongside the results of `list_aml_monitors`. Your agent then uses both to generate an answer. This approach prevents hallucinations because every answer is grounded in concrete data from the SEON MCP Server tools.

LlamaIndex: Analyzing Identity Records

Gather and index identity details using `check_ip` or `get_account_info`. By indexing these results, you can perform semantic searches across millions of records. You're not just getting a list; you're finding patterns in the data that link seemingly unrelated accounts. This capability lets you query historical intelligence effortlessly, making your knowledge base far more powerful.

Setup guide

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

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Common questions about SEON MCP in LlamaIndex

LlamaIndex indexes the output from tools like `check_fraud`. Instead of just passing a score, it stores the reasoning and the raw results, allowing you to query the *why* behind the risk score later.

You build deep research applications. You can combine structured data—like `get_transaction` details—with unstructured documents to give users highly specific, context-aware answers.

Yes. By indexing the results of `get_transaction`, you can query past activity and get answers grounded in actual API data, which is perfect for compliance checks.

This SEON server touches identity types like phone numbers and email addresses. When indexing, make sure your vector store handles the sensitivity of that PII to maintain compliance.

It does. You can index the outputs from `list_rules` alongside procedural documents. This lets you query and understand exactly how different fraud rules apply across various scenarios.

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