How to Use the Dastra MCP in LlamaIndex

Q: How do I filter Dastra tools in LlamaIndex?

Use the allowedtools filter during the agent setup phase. This limits what the agent can access in your account.

Ground LlamaIndex RAG in your privacy data. Index Dastra records to get accurate answers about your compliance posture.

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Connect Dastra MCP to LlamaIndex

Create your Vinkius account to connect Dastra 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 Dastra with LlamaIndex

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Index Dastra records for LlamaIndex RAG

Turn your privacy datasets into a searchable knowledge base. By calling `list_datasets` and `list_workspaces`, LlamaIndex ingests your actual configuration into a vector store. You avoid hallucinations because your AI client answers questions based on live data from your account. It finds the exact record you need without scanning every file manually.

Query DSR status within LlamaIndex

Ask your agent about specific Data Subject Requests using `get_dsr`. LlamaIndex retrieves the record and makes it available for semantic search alongside your other documents. This gives your team a unified view of all pending requests. You get grounded, accurate summaries of compliance status in seconds.

Search processing activities with LlamaIndex

Pull your entire processing inventory using `list_processings`. LlamaIndex indexes these activities so you can ask natural language questions about your data mapping. You build a RAG pipeline that understands your company's privacy footprint. It connects the dots between your datasets and your legal obligations.

Setup guide

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

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Common questions about Dastra MCP in LlamaIndex

Use the MCP tool spec to fetch the tools from your Vinkius server. Pass those tools into your agent so it can pull the data for indexing.

No, because the tools fetch live data that you index into your vector store. The AI answers based on the records it just retrieved.

It can. You use the listing tools to pull the workspace data and then feed it into the FunctionAgent.

Use the allowed_tools filter during the agent setup phase. This limits what the agent can access in your account.

Your DSR and breach data stays inside your defined index. We manage the secure tunnel between your LlamaIndex setup and the Vinkius hosting environment.

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