How to Use the DVC MCP in LlamaIndex

Q: Can I use LlamaIndex to search through my DVC experiment logs?

Yes. The agent calls listexperiments to retrieve the logs, which LlamaIndex then indexes into a vector store. This lets you run semantic queries over your training history.

Q: How do I configure LlamaIndex to pull data from a specific DVC view?

Use the getview tool inside your MCP client setup. The agent retrieves the view details and processes the structure as a document source for your index.

Q: Does LlamaIndex update its index when a DVC project changes?

You can trigger the agent to run getproject periodically. It fetches the latest project state and updates the vector store with the new metadata automatically.

Q: Can I limit which tools the LlamaIndex agent can access?

Yes, you can use the allowedtools filter during the MCP Server setup. This lets you restrict the agent to read-only tools like listprojects and listviews if needed.

Index your DVC experiment history and dataset views directly into LlamaIndex vector stores.

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Index live DVC project states directly in LlamaIndex

Stop writing custom scripts to parse your training metadata. Your agent uses this MCP Server to call `list_projects` and `get_project` to fetch active workspace details. LlamaIndex then indexes this live data directly into your vector store, allowing you to query your project setup using natural language. This turns static configuration files into a searchable knowledge base. Your agent quickly finds which project contains specific parameters without manual searching.

Query experiment histories without hallucinations

Ground your agent's answers in actual training logs. The agent uses `list_experiments` to extract the full run history, converting the raw metrics into indexable documents. When you ask which model performed best on a specific metric, the agent queries the index instead of guessing. It pulls the exact run details directly from your DVC history.

Semantic search over dataset views

Map your data splits to your natural language queries. By calling `list_views` and `get_view`, the agent retrieves the exact data definitions used in your pipeline. These views are indexed alongside your code. You can ask which dataset slice was used for a specific training run, and the agent retrieves the exact view metadata instantly.

Setup guide

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

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Common questions about DVC MCP in LlamaIndex

Yes. The agent calls `list_experiments` to retrieve the logs, which LlamaIndex then indexes into a vector store. This lets you run semantic queries over your training history.

Use the `get_view` tool inside your MCP client setup. The agent retrieves the view details and processes the structure as a document source for your index.

You can trigger the agent to run `get_project` periodically. It fetches the latest project state and updates the vector store with the new metadata automatically.

Yes, you can use the allowed_tools filter during the MCP Server setup. This lets you restrict the agent to read-only tools like `list_projects` and `list_views` if needed.

Yes, the server operates under a zero-trust architecture. Your project configurations, user profiles from `get_user`, and experiment parameters are processed entirely in memory and never persisted.

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