How to Use the Neptune.ai (ML Experiment Tracking) MCP in LlamaIndex

Index your experiment history into searchable knowledge bases with Neptune.ai (ML Experiment Tracking) and LlamaIndex.

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Connect Neptune.ai (ML Experiment Tracking) MCP to LlamaIndex

Create your Vinkius account to connect Neptune.ai (ML Experiment Tracking) 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 Neptune.ai (ML Experiment Tracking) with LlamaIndex

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Ground your RAG pipeline in actual run data

Call `search_runs` to grab raw experiment logs and index them. Your RAG application now uses real-time API data rather than static documents. This ensures your answers reflect the latest training runs. The agent retrieves the metadata and embeds it into your vector store for semantic retrieval.

Build a searchable model registry

Use `list_models` to pull your registered models into the index. Your application can now answer questions about model versions and status based on the latest server state. This turns your experiment tracker into a queryable knowledge source. You no longer have to hunt through the UI to find which model version performed best.

Query past experiments with semantic search

LlamaIndex takes the output from `get_attributes` and makes it searchable. You ask questions about why a run failed, and the system finds the relevant parameters. This creates a bridge between your raw experiment data and your natural language queries. The index handles the heavy lifting of finding patterns across hundreds of runs.

Setup guide

Set up Neptune.ai (ML Experiment Tracking) 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 Neptune.ai (ML Experiment Tracking) 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 Neptune.ai (ML Experiment Tracking) tools.",
)
response = await agent.run("List recent Neptune.ai (ML Experiment Tracking) data")

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Common questions about Neptune.ai (ML Experiment Tracking) MCP in LlamaIndex

You use the MCP tool spec to fetch the data. Once the agent has the results, you pass them to your vector store as part of your document ingestion process.

Yes. The tool output is structured text that LlamaIndex parses and embeds. You can then perform semantic searches across your run history.

Yes. The agent fetches the latest run attributes when you ask a question. It ensures your knowledge base stays current with your actual ML experiments.

You just need to provide your API token to the server. The connection is handled through the standard MCP adapter without extra configuration.

All traffic is isolated within the Vinkius sandbox. Your experiment data is encrypted during transit and never exposed to the public internet without your authorized token.

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