How to Use the Openli MCP in LlamaIndex

Index your Openli compliance logs and agreements directly into LlamaIndex for semantic search and accurate RAG queries.

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Connect Openli MCP to LlamaIndex

Create your Vinkius account to connect Openli 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 Openli with LlamaIndex

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Ground your legal queries in real compliance data

Stop letting your agent hallucinate about your privacy policies. This MCP Server lets LlamaIndex index the live output of `list_agreements` and `get_agreement` directly into your vector store. When a user asks about your terms, the query engine searches the actual, active legal documents instead of guessing. This turns your static legal files into a dynamic, searchable knowledge base. Your agent answers customer questions using the exact text stored in your compliance registry, ensuring accuracy.

Search and analyze historical consent records

Keep your legal team informed by making your consent history searchable. LlamaIndex pulls records using `list_consents` and indexes them so you can run semantic queries over thousands of user actions. You can quickly verify patterns or find specific consent events without writing complex SQL queries. This makes auditing your compliance history incredibly simple. The agent uses the `get_consent` tool to pull specific details when a deep dive is required, combining structured data with semantic search.

Automate vendor risk assessments

Your agent can index your vendor directory to check for compliance gaps. By calling `list_vendors`, LlamaIndex ingests the metadata of your third-party processors. It then compares this information against your internal privacy standards to highlight potential risks. If a vendor's details change, the agent uses `get_vendor` to update the index. This ensures your risk assessments are always based on live, accurate data rather than outdated spreadsheets.

Setup guide

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

You use the LlamaIndex MCP tool spec to fetch data from the server. The framework calls `list_agreements` to retrieve your legal documents and loads them into a vector store. This makes your actual privacy terms queryable by your RAG pipeline.

Yes, your query engine can search through past data subject requests. By indexing the output of `list_dsars`, LlamaIndex lets you search for specific requests using natural language. The agent can then use `get_dsar` to retrieve the full, unindexed details of a specific record.

You can configure your index to refresh periodically by calling the tools on a schedule. This ensures that new agreements created via `create_agreement` are indexed immediately. Your agent will always answer queries using the most current legal text.

You convert the MCP tools into LlamaIndex tool specs and pass them to your FunctionAgent. The agent can then choose to run tools like `save_consent` when a user updates their preferences during a chat session. This bridges the gap between search and action.

The system handles sensitive data like DSAR details and consent records locally within your secure runtime. When calling `get_dsar` or `list_audit_logs`, the payload is processed directly by your client without passing through external intermediate servers. This keeps your compliance records private and secure.

Use it with your favorite AI tools

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