DVC MCP Server for LlamaIndex 6 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add DVC 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 MCP SERVER
Vinkius supports streamable HTTP and SSE.
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 DVC. "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in DVC?"
)
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 DVC MCP Server
Connect your DVC Studio account to any AI agent and take full control of your machine learning experiments and data versioning workflows through natural conversation.
LlamaIndex agents combine DVC tool responses with indexed documents for comprehensive, grounded answers. Connect 6 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
- Project Orchestration — Expose registered organization workspaces and validate available physical repositories connected within DVC Studio limits
- Experiment Navigation — Iterate through explicitly generated model runs mapping precise metric arrays and discovering logged metrics history cleanly
- View Management — Extract explicit UI configuration layouts and dashboard settings to retrieve structural workspace representations natively
- Repository Auditing — Analyze specific identifier boundaries resolving internal team mappings and parsing direct repository metadata constraints
- Metric Inspection — Retrieve complex structural arrays defining precisely which metrics were captured during specific experiment epochs
- Identity Oversight — Identify the exact authorized token holder exposing mapping roles and organization scopes dynamically to verify permissions
The DVC MCP Server exposes 6 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.
How to Connect DVC to LlamaIndex via MCP
Follow these steps to integrate the DVC MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 6 tools from DVC
Why Use LlamaIndex with the DVC MCP Server
LlamaIndex provides unique advantages when paired with DVC through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine DVC tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain DVC tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query DVC, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what DVC tools were called, what data was returned, and how it influenced the final answer
DVC + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the DVC MCP Server delivers measurable value.
Hybrid search: combine DVC real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query DVC 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 DVC for fresh data
Analytical workflows: chain DVC queries with LlamaIndex's data connectors to build multi-source analytical reports
DVC MCP Tools for LlamaIndex (6)
These 6 tools become available when you connect DVC to LlamaIndex via MCP:
get_project
Get project
get_user
Get user profile
get_view
Get view
list_experiments
List experiments
list_projects
List projects
list_views
List views
Example Prompts for DVC in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with DVC immediately.
"List all projects in my DVC Studio account"
"Show me the last 5 experiments for project 'Credit-Scoring-Model'"
"What are my dashboard views in DVC?"
Troubleshooting DVC MCP Server with LlamaIndex
Common issues when connecting DVC to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpDVC + LlamaIndex FAQ
Common questions about integrating DVC MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect DVC with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect DVC to LlamaIndex
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.