How to Use the DataScope MCP in LlamaIndex

Index your field audit results into LlamaIndex for semantic search and grounded answers.

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Connect DataScope MCP to LlamaIndex

Create your Vinkius account to connect DataScope 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 DataScope with LlamaIndex

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Index DataScope answers in LlamaIndex

Convert `list_form_submissions` output into vector embeddings. Your agent searches these results to find specific audit answers from the past. Stop guessing what field workers wrote. Query your index and get facts pulled from your actual form history.

Query user data via LlamaIndex

Run `list_organization_users` to populate your knowledge base with team details. Your agent then answers questions about who submitted which form. This links your people to their data. Your RAG pipeline becomes the central hub for all field operations.

Map locations in LlamaIndex

Use `list_tracked_locations` to feed site history into your vector store. Your agent identifies patterns in where your audits actually happen. Your index stays live. Any update in the field reflects in your searchable knowledge base automatically.

Setup guide

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

Use the McpToolSpec to load tools into your agent. Once loaded, you can ingest the tool outputs into your vector store.

You can filter submissions by ID using the provided tools. Pass these filters to the agent to restrict the scope of your index.

We use encrypted transport for all form data. Your indexed content resides only where you store your vector database.

Yes, once you index the submissions, you can query across all forms. The agent resolves the context from your stored vectors.

It accesses form metadata, submission answers, and user lists. This data is transmitted via a unique, single-use session token.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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