How to Use the Eden AI Alternative MCP in Google ADK

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

Use the McpToolset class from the SDK to point to the MCP Server endpoint. Pass this toolset directly to your LlmAgent to expose all 13 model routing and file tools.

Q: Can I monitor costs when using the Eden AI Alternative and Google ADK MCP Server?

Yes, you can call checkcredits and monitorconsumption to track your spend. This allows your agent to halt execution if your budget thresholds are crossed.

Q: How do I handle file storage with this setup?

You upload files using uploadfile and list active resources with listfiles. The agent can then pass these file references to various translation and extraction engines.

Q: How does this MCP Server protect my document data?

All files handled by uploadfile are stored in isolated, ephemeral environments. We do not use your documents to train models, and they are deleted permanently when you call deletefiles.

Connect Gemini agents to 100+ models and specialized document tools using the Google ADK.

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Connect Eden AI Alternative MCP to Google ADK

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

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Run translation and OCR inside Google ADK

The `universal_ai_sync` tool executes translation, OCR, and image generation tasks directly within your agent workflows. Your Gemini model decides when to trigger these specialized engines based on incoming file formats. For long-running processes like audio transcription, the `universal_ai_async` tool starts background operations. Your agent monitors these tasks with `get_async_job` to keep your main loop responsive.

Route LLMs dynamically using this MCP Server

The `chat_completions` tool lets you access alternative language models when Gemini needs a specialized fallback. You route prompts to specific providers without rewriting your Google Cloud integration code. To keep conversation state intact during these handoffs, the `create_stateful_response` tool stores history on the server. This prevents context loss when switching between different backend models mid-session.

Generate text embeddings and manage files

The `create_embedding` tool converts text into vectors for search and retrieval tasks. You can query `list_embedding_models` to see which vector models are currently active in your environment. File operations are handled through `upload_file` and `delete_files` to keep your storage clean. This ensures your agent can process documents without cluttering your persistent database.

Setup guide

Set up Eden AI Alternative 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 Alternative 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 Alternative_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Eden AI Alternative tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Eden AI Alternative MCP in Google ADK

Use the `McpToolset` class from the SDK to point to the MCP Server endpoint. Pass this toolset directly to your `LlmAgent` to expose all 13 model routing and file tools.

It removes the need to maintain credentials for dozens of separate AI providers. You connect once and let the toolset handle the translation of schemas and payloads.

Yes, you can call `check_credits` and `monitor_consumption` to track your spend. This allows your agent to halt execution if your budget thresholds are crossed.

You upload files using `upload_file` and list active resources with `list_files`. The agent can then pass these file references to various translation and extraction engines.

All files handled by `upload_file` are stored in isolated, ephemeral environments. We do not use your documents to train models, and they are deleted permanently when you call `delete_files`.

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