How to Use the Anura MCP in LlamaIndex

Q: How do I filter Anura results in LlamaIndex?

You define an allowedtools list in your client configuration. This keeps your agent focused on the relevant security functions.

Build a searchable knowledge base of fraud reports using LlamaIndex and Anura.

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Connect Anura MCP to LlamaIndex

Create your Vinkius account to connect Anura 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 Anura with LlamaIndex

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Index Anura data with LlamaIndex

Turn your fraud logs into a queryable knowledge base. Using `get_system_status`, you feed live visitor insights directly into your vector store. LlamaIndex stores these results so your agent can compare current traffic against historical fraud patterns. It turns raw API output into long-term context.

Ground your RAG in fraud data

Stop guessing why a visitor is suspicious. Your LlamaIndex application fetches the latest status from Anura and uses it to ground your final response. This creates a system where the AI answers questions based on actual system integrity. Hallucinations drop when the context is tied to live security checks.

Query your security history

Search past sessions to identify trends in bot activity. Since the MCP server output is indexed, you can ask your agent about patterns from last week. This provides a historical view of your traffic quality. You get to see if specific campaigns are attracting more fraud over time.

Setup guide

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

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Common questions about Anura MCP in LlamaIndex

You use the MCP tool spec to pull the status and pass it to your data pipeline. LlamaIndex then embeds this information into your vector index for retrieval.

It provides source-of-truth data for your security queries. By grounding the agent in actual Anura fraud reports, you ensure it relies on facts.

Yes, you wrap the tool in a spec and pass it to the function agent. It becomes a searchable resource for your RAG pipeline.

You define an allowed_tools list in your client configuration. This keeps your agent focused on the relevant security functions.

All traffic is isolated within your Vinkius sandbox. Your LlamaIndex application only accesses the specific visitor status records you explicitly request.

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