How to Use the Autenti MCP in LlamaIndex

Q: How do I configure Autenti with LlamaIndex?

Install llama-index-tools-mcp via pip. Set up a BasicMCPClient pointing to your Vinkius URL, wrap it in McpToolSpec, and await totoollistasync(). Those tools go directly into your FunctionAgent.

Q: Can LlamaIndex search my Autenti contacts?

Yes. Your agent calls listsavedcontacts and embeds the returned address book into your vector store. You can then search for signers using natural language queries.

Q: Does this MCP Server work with LlamaIndex data access filters?

You can strictly control what the agent sees. Pass the allowedtools filter when initializing the client to restrict access. If you only want read-only queries, just expose the list and get tools.

Q: How does the agent handle complex eIDAS processes?

It breaks them down step-by-step. The system might use getprocessdetails to understand the current state before answering a prompt. This ensures your knowledge base reflects the exact legal standing of the document.

Index live eIDAS signature data directly into your LlamaIndex knowledge base using this Autenti MCP integration.

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Connect Autenti MCP to LlamaIndex

Create your Vinkius account to connect Autenti 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 Autenti with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

RAG over live contract status

Static documents tell half the story. You can point LlamaIndex at `list_signature_processes` to pull real-time agreement states into your vector store. Users query the agent about pending deals and get answers backed by live API data. Grounding responses in reality prevents hallucinations. When a user asks who needs to sign next, the system runs `list_process_participants` and indexes the exact names. The answer matches the actual European eIDAS record perfectly.

Index webhooks and files

System configurations often get lost in documentation. Your LlamaIndex application can ingest active configurations by calling `list_configured_webhooks`. Developers query the index to understand exactly where contract events are firing. The same applies to the documents themselves. Running `list_process_files` feeds the file metadata straight into your retrieval pipeline. You build a unified search experience across both the contract text and its legal execution status.

Query the LlamaIndex MCP Server

Sometimes you just need to know what actions are legally possible right now. The agent executes `list_available_process_actions` and caches the results. Users interact with a natural language interface to see if they can reject or approve a specific document. Execution happens through the same interface. Once the user decides, the agent fires `execute_process_action` to finalize the step. You get a complete read-write loop powered by semantic search and strict legal guardrails.

Setup guide

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Autenti. 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 Autenti MCP in LlamaIndex

Install `llama-index-tools-mcp` via pip. Set up a `BasicMCPClient` pointing to your Vinkius URL, wrap it in `McpToolSpec`, and await `to_tool_list_async()`. Those tools go directly into your `FunctionAgent`.

Yes. Your agent calls `list_saved_contacts` and embeds the returned address book into your vector store. You can then search for signers using natural language queries.

You can strictly control what the agent sees. Pass the `allowed_tools` filter when initializing the client to restrict access. If you only want read-only queries, just expose the list and get tools.

It breaks them down step-by-step. The system might use `get_process_details` to understand the current state before answering a prompt. This ensures your knowledge base reflects the exact legal standing of the document.

We run your connection through a zero-trust V8 Isolate. Your agent retrieves highly sensitive eIDAS compliance logs and participant emails, but nothing persists on our end. The isolated environment evaporates instantly after your RAG pipeline finishes its retrieval step.

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