How to Use the Eden AI MCP in Google ADK

Q: How do I configure Eden AI with Google ADK?

Install google-adk via pip. Initialize a McpToolset using your Vinkius HTTP endpoint URL. Pass that toolset to your LlmAgent constructor.

Q: Can my Google ADK agent see all my Eden AI models?

Yes. The agent simply calls listallllmmodels to fetch the complete list. It can then use Gemini's long context to analyze the capabilities of every available model.

Q: How does Eden AI work with Google ADK BigQuery pipelines?

Your agent can pull consumption metrics via getapiusagestatistics and write those records straight to BigQuery. This gives your finance team a clear view of cross-provider spending.

Q: What tools check provider availability?

The server provides listavailableaifeatures and quickaiprovideraudit. These let your agent map out which vendors support specific tasks like image generation or text analysis.

Q: How is my Eden AI credit data protected?

When your agent calls getedenaimetadata, the transaction occurs within an ephemeral zero-trust environment. Vinkius handles the authentication layer, meaning your raw API tokens never touch the Google Cloud execution context.

Give your Google ADK agents direct access to Eden AI workflows and cross-provider analytics via this MCP server.

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Connect Eden AI MCP to Google ADK

Create your Vinkius account to connect Eden AI to Google ADK 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 Eden AI with Google ADK

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Map unified pipelines with this MCP Server

Your Gemini agent calls `list_ai_workflows` to pull active automation pipelines directly into its context window. Enterprise agents on Google Cloud handle massive contexts, but they still need to know what workflows exist. It checks the environment before taking action. When an agent needs specifics, it uses `get_workflow_configuration` to parse the exact steps of a target pipeline. You can even track recent changes by invoking `list_latest_ai_automations`. The agent knows exactly how a workflow is structured before pushing data into it.

Audit vendors and compare pricing

Your agent uses `get_ai_feature_pricing` to pull cost data across different vendors for a specific task. Running models at scale gets expensive. It compares the numbers and selects the most efficient route. The `quick_ai_provider_audit` tool gives your agent a high-level summary of text analysis providers. You also need to know who is available. If you need a deeper look, `list_ai_providers` returns the full roster of active vendors for any given feature.

Monitor API limits and usage stats

Your agent triggers `get_api_usage_statistics` to retrieve technical stats on your API consumption. You cannot manage what you do not measure. It feeds this data directly into your BigQuery tables for long-term tracking. The `get_eden_ai_metadata` tool checks your current credit balance to ensure account health. If the numbers look low, the agent can pause non-critical background jobs until finance approves a top-up.

Setup guide

Set up Eden AI MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Eden AI tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Eden AI_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Eden AI tools via MCP.",
    tools=mcp_tools,
)

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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 Eden AI. 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 Eden AI MCP in Google ADK

Install `google-adk` via pip. Initialize a `McpToolset` using your Vinkius HTTP endpoint URL. Pass that toolset to your `LlmAgent` constructor.

Yes. The agent simply calls `list_all_llm_models` to fetch the complete list. It can then use Gemini's long context to analyze the capabilities of every available model.

Your agent can pull consumption metrics via `get_api_usage_statistics` and write those records straight to BigQuery. This gives your finance team a clear view of cross-provider spending.

The server provides `list_available_ai_features` and `quick_ai_provider_audit`. These let your agent map out which vendors support specific tasks like image generation or text analysis.

When your agent calls `get_eden_ai_metadata`, the transaction occurs within an ephemeral zero-trust environment. Vinkius handles the authentication layer, meaning your raw API tokens never touch the Google Cloud execution context.

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