How to Use the IPinfo MCP in LlamaIndex

Q: Can I index IPinfo geolocation data into a LlamaIndex vector store?

Yes, you can write the output of getipdetails directly into LlamaIndex document objects. The framework embedding model then converts these geographic and network details into searchable vector representations.

Q: How does LlamaIndex handle historical IPinfo lookups?

Your agent invokes getiphistorical to retrieve past ownership records for a specific IP. LlamaIndex stores this historical data in your index, allowing semantic queries to trace past network changes.

Index live IP telemetry directly into your LlamaIndex vector store for semantic security queries.

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Connect IPinfo MCP to LlamaIndex

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Setup IPinfo with LlamaIndex

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Build a searchable index of network data

`get_ip_details` pulls geographic coordinates and ISP details to populate your document store via the MCP tool spec. The framework indexes this raw network metadata so your agent can query past lookups without making redundant API calls. By combining this with `get_asn_details`, you build a semantic knowledge base of network origins. Your RAG pipelines query this index to find patterns in malicious traffic based on historical data.

Expose privacy flags to LlamaIndex agents

`get_privacy_details` identifies active VPNs, proxies, and Tor exit nodes to feed your index. The framework processes these privacy flags, turning raw JSON responses into searchable vector embeddings. Your agent queries this knowledge base to flag incoming connections that share similar risk profiles. This approach replaces static lookup tables with dynamic, semantic risk evaluation.

Audit network ranges with this MCP Server

`get_ip_range` retrieves the full CIDR block metadata to help your LlamaIndex agent map out entire subnets. The agent indexes these ranges, making it easy to query which blocks belong to specific hosting providers. When paired with `get_ip_historical`, your agent can track how ownership of these ranges changed over time within your LlamaIndex MCP setup. This setup ensures your RAG pipeline always works with updated, structured network context.

Setup guide

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

Initialize the BasicMCPClient with your Vinkius endpoint, then wrap it in McpToolSpec. Call to_tool_list_async() to convert the tools and pass them to your LlamaIndex FunctionAgent.

Yes, you can write the output of get_ip_details directly into LlamaIndex document objects. The framework embedding model then converts these geographic and network details into searchable vector representations.

Your agent invokes get_ip_historical to retrieve past ownership records for a specific IP. LlamaIndex stores this historical data in your index, allowing semantic queries to trace past network changes.

You can set up node postprocessors in LlamaIndex to filter out nodes containing privacy flags from get_privacy_details. This prevents VPN or proxy metadata from polluting your clean retrieval context.

Only the specific IP addresses, ASNs, or CIDR ranges you pass to tools like get_ip_details are processed. Vinkius runs the server in an ephemeral sandbox, ensuring your network metadata is never written to persistent logs or shared.

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