Dastra MCP Server for LlamaIndexGive LlamaIndex instant access to 12 tools to Archive Dsr, Create Breach, Create Dsr, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Dastra as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
Ask AI about this App Connector for LlamaIndex
The Dastra app connector for LlamaIndex is a standout in the Document Management category — giving your AI agent 12 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Dastra. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in Dastra?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Dastra MCP Server
Connect your Dastra account to any AI agent and take full control of your data privacy and GDPR/LGPD compliance workflows through natural conversation.
LlamaIndex agents combine Dastra tool responses with indexed documents for comprehensive, grounded answers. Connect 12 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- DSR Orchestration — Manage the full lifecycle of Data Subject Requests (DSR) programmatically, from creation and status tracking to high-fidelity resolution and archiving
- Breach & Incident Tracking — Programmatically document and track security incidents and data breaches to maintain a perfectly coordinated regulatory response
- ROPA & Asset Intelligence — Access your Record of Processing Activities (ROPA) and documented datasets to oversee data flow and classification across workspaces
- Compliance Architecture — Retrieve complete directories of actors (controllers, processors), tags, and processing activities to maintain a high-fidelity privacy registry
- Workspace Visibility — List and manage multiple workspaces programmatically to coordinate privacy operations across different business units directly through your agent
The Dastra MCP Server exposes 12 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
All 12 Dastra tools available for LlamaIndex
When LlamaIndex connects to Dastra through Vinkius, your AI agent gets direct access to every tool listed below — spanning gdpr, data-privacy, dsr-management, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Archive a DSR
Create a new data breach record
Create a new Data Subject Request
Get details for a specific DSR
). List actors
List data breaches
List datasets
List Data Subject Requests
List processing activities (ROPA)
List workspace tags
List all workspaces
Update an existing DSR
Connect Dastra to LlamaIndex via MCP
Follow these steps to wire Dastra into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the Dastra MCP Server
LlamaIndex provides unique advantages when paired with Dastra through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Dastra tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Dastra tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Dastra, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Dastra tools were called, what data was returned, and how it influenced the final answer
Dastra + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Dastra MCP Server delivers measurable value.
Hybrid search: combine Dastra real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Dastra to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Dastra for fresh data
Analytical workflows: chain Dastra queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Dastra in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Dastra immediately.
"List all active workspaces in my Dastra account."
"Show pending DSR requests for workspace 'ws_123'."
"Register a new data breach 'Server Unauthorized Access' in workspace 'ws_123'."
Troubleshooting Dastra MCP Server with LlamaIndex
Common issues when connecting Dastra to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpDastra + LlamaIndex FAQ
Common questions about integrating Dastra MCP Server with LlamaIndex.