How to Use the ThinkStack MCP in Google ADK

Q: What tools are available for managing bot definitions with ThinkStack and Google ADK?

You have listbots to see everything, plus getbot for deep dives. You can also check the full capabilities of the bots using listactions. It’s a solid set of management tools.

Q: Can I verify the connectivity status of ThinkStack and Google ADK?

Yes. The checkthinkstackstatus tool lets your agent ping the API endpoints. It confirms everything is up and running before starting a major workflow.

Q: How do I list all connected knowledge sources for ThinkStack and Google ADK?

Use listsources. This command provides an inventory of every single piece of data the agent has indexed. It’s your central check on data availability.

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Connect ThinkStack MCP to Google ADK

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Setup ThinkStack with Google ADK

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Perplexity

Execute complex queries

`send_query` lets your agent send input to the chatbot and get a result back. Because of its strong connection to Google Cloud, it handles large-scale reasoning across massive data sets. It’s built for enterprise scale, allowing the Gemini models to process complex requests that hold deep context.

Maintain knowledge bases

The `add_source` tool lets you index new content directly into your agent's knowledge base. This is how your system stays current with enterprise-level data updates. Need to prune old data? Use `delete_source`. It gives you fine-grained control over the information the agent relies on.

Audit bot and conversation history

You can see which bots exist by listing them with `list_bots`, or check specific details using `get_bot`. To track interactions, run `list_conversations` to get a full view of the chat log. This means you can trace every step and action taken on behalf of the user.

Setup guide

Set up ThinkStack 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 ThinkStack 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="ThinkStack_agent",
    model="gemini-2.0-flash",
    instruction="You have access to ThinkStack tools via MCP.",
    tools=mcp_tools,
)

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Common questions about ThinkStack MCP in Google ADK

The system supports long-context reasoning, letting your agent handle 1M+ tokens. This means it can draw connections across enormous amounts of enterprise data stored in sources.

You have `list_bots` to see everything, plus `get_bot` for deep dives. You can also check the full capabilities of the bots using `list_actions`. It’s a solid set of management tools.

This server manages conversation details, including chat history. Because this is for enterprise clients, you'll want to ensure all source material remains compliant with internal governance rules.

Yes. The `check_thinkstack_status` tool lets your agent ping the API endpoints. It confirms everything is up and running before starting a major workflow.

Use `list_sources`. This command provides an inventory of every single piece of data the agent has indexed. It’s your central check on data availability.

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