Neptune.ai (ML Experiment Tracking) MCP Server for Google ADK 6 tools — connect in under 2 minutes
Google Agent Development Kit (ADK) is Google's framework for building production AI agents. Add Neptune.ai (ML Experiment Tracking) as an MCP tool provider through Vinkius and your ADK agents can call every tool with full schema introspection.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
from google.adk.agents import Agent
from google.adk.tools.mcp_tool import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import (
StreamableHTTPConnectionParams,
)
# Your Vinkius token. get it at cloud.vinkius.com
mcp_tools = McpToolset(
connection_params=StreamableHTTPConnectionParams(
url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
)
)
agent = Agent(
model="gemini-2.5-pro",
name="neptuneai_ml_experiment_tracking_agent",
instruction=(
"You help users interact with Neptune.ai (ML Experiment Tracking) "
"using 6 available tools."
),
tools=[mcp_tools],
)
* 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 Neptune.ai (ML Experiment Tracking) MCP Server
Connect your Neptune.ai account to any AI agent and take full control of your machine learning experimentation, model versioning, and training telemetry through natural conversation.
Google ADK natively supports Neptune.ai (ML Experiment Tracking) as an MCP tool provider. declare Vinkius Edge URL and the framework handles discovery, validation, and execution automatically. Combine 6 tools with Gemini's long-context reasoning for complex multi-tool workflows, with production-ready session management and evaluation built in.
What you can do
- Experiment Orchestration — List all managed ML projects and retrieve detailed metadata configurations tracking active runs and workspace boundaries directly from your agent
- Run Audit & Search — Discover specific training runs or historical experiment state checkpoints mapping deep ML parameter sets and performance bounds securely
- Attribute Inspection — Extract detailed telemetry capturing the exact variables, accuracy metrics, and loss curves logged during specific execution checkpoints natively
- Model Registry Management — List and retrieve trained tracking models promoted and logged explicitly, isolating stable versions from ephemeral experimentation runs
- Organizational Visibility — Enumerate accessible workspaces and projects to understand your ML research footprint and documentation distribution natively
- Credential Audit — Verify specific user identifies and availability details bound inherently against your active service account token securely
- Metadata Retrieval — Deep-dive into specific Project or Run IDs to retrieve precise JSON representations and chronological experimentation insights instantly
The Neptune.ai (ML Experiment Tracking) MCP Server exposes 6 tools through the Vinkius. Connect it to Google ADK 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 Neptune.ai (ML Experiment Tracking) to Google ADK via MCP
Follow these steps to integrate the Neptune.ai (ML Experiment Tracking) MCP Server with Google ADK.
Install Google ADK
Run pip install google-adk
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Create the agent
Save the code above and integrate into your ADK workflow
Explore tools
The agent will discover 6 tools from Neptune.ai (ML Experiment Tracking) via MCP
Why Use Google ADK with the Neptune.ai (ML Experiment Tracking) MCP Server
Google ADK provides unique advantages when paired with Neptune.ai (ML Experiment Tracking) through the Model Context Protocol.
Google ADK natively supports MCP tool servers. declare a tool provider and the framework handles discovery, validation, and execution
Built on Gemini models, ADK provides long-context reasoning ideal for complex multi-tool workflows with Neptune.ai (ML Experiment Tracking)
Production-ready features like session management, evaluation, and deployment come built-in. not bolted on
Seamless integration with Google Cloud services means you can combine Neptune.ai (ML Experiment Tracking) tools with BigQuery, Vertex AI, and Cloud Functions
Neptune.ai (ML Experiment Tracking) + Google ADK Use Cases
Practical scenarios where Google ADK combined with the Neptune.ai (ML Experiment Tracking) MCP Server delivers measurable value.
Enterprise data agents: ADK agents query Neptune.ai (ML Experiment Tracking) and cross-reference results with internal databases for comprehensive analysis
Multi-modal workflows: combine Neptune.ai (ML Experiment Tracking) tool responses with Gemini's vision and language capabilities in a single agent
Automated compliance checks: schedule ADK agents to query Neptune.ai (ML Experiment Tracking) regularly and flag policy violations or configuration drift
Internal tool platforms: build self-service agent platforms where teams connect their own MCP servers including Neptune.ai (ML Experiment Tracking)
Neptune.ai (ML Experiment Tracking) MCP Tools for Google ADK (6)
These 6 tools become available when you connect Neptune.ai (ML Experiment Tracking) to Google ADK via MCP:
get_attributes
Get parameters mapped within an experiment runtime bounds
get_project
Get specific details for a targeted Neptune ML project
get_user
Get specific user credentials and availability details
list_models
List trained tracking models packaged natively within a project
list_projects
List accessible Neptune workspaces and projects
search_runs
Search explicitly tracked ML experimentation runs inside a project
Example Prompts for Neptune.ai (ML Experiment Tracking) in Google ADK
Ready-to-use prompts you can give your Google ADK agent to start working with Neptune.ai (ML Experiment Tracking) immediately.
"List all training runs for the 'Customer-Churn' project"
"Show me the metrics for run ID 'churn-exp-123'"
"List all registered models in project 'Fraud-Detection'"
Troubleshooting Neptune.ai (ML Experiment Tracking) MCP Server with Google ADK
Common issues when connecting Neptune.ai (ML Experiment Tracking) to Google ADK through the Vinkius, and how to resolve them.
McpToolset not found
pip install --upgrade google-adkNeptune.ai (ML Experiment Tracking) + Google ADK FAQ
Common questions about integrating Neptune.ai (ML Experiment Tracking) MCP Server with Google ADK.
How does Google ADK connect to MCP servers?
Can ADK agents use multiple MCP servers?
Which Gemini models work best with MCP tools?
Connect Neptune.ai (ML Experiment Tracking) with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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Google's framework for building production AI agents.
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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 Neptune.ai (ML Experiment Tracking) to Google ADK
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.