How to Use the Alchemer MCP in Google ADK

Q: Can I filter Alchemer data for my Google ADK agent?

You can use the toolnames filter to limit what the agent sees. This prevents the agent from accessing tools like getaccountusage if you only want it to read responses.

Use Google ADK to query Alchemer feedback and pipe it straight into your BigQuery data pipelines.

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Connect Alchemer MCP to Google ADK

Create your Vinkius account to connect Alchemer 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 Alchemer with Google ADK

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Query Alchemer from Google ADK

Connect your Gemini-powered agents to your feedback repository using `list_surveys`. Your agent can now browse and select targets for analysis. Once you pick a survey, `list_survey_responses` fetches the raw content. This allows your agent to process large volumes of feedback within a single context window.

Automated reporting for Google ADK

Invoke `list_survey_reports` to grab summary data for your internal dashboards. Your agent pulls the numbers needed to update your metrics automatically. Use `get_survey_details` to verify the state of a project before running reports. It keeps your reporting logic grounded in accurate metadata.

Targeted feedback retrieval

Extract precise details with `get_question_details` to refine how your agent parses survey results. You define the logic, the tool provides the data. Check your account health with `get_account_usage` to monitor your current activity levels. This gives your agent visibility into your active Alchemer footprint.

Setup guide

Set up Alchemer 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 Alchemer 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="Alchemer_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Alchemer 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 Alchemer. 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 Alchemer MCP in Google ADK

You use the McpToolset class to bridge the server to your agent. This allows the Gemini model to call tools like `list_surveys` as needed.

You can use the tool_names filter to limit what the agent sees. This prevents the agent from accessing tools like `get_account_usage` if you only want it to read responses.

The server provides the data, and your agent handles the token budget. You can pull massive response sets via `list_survey_responses` to fill that 1M+ token window.

You pass the toolset to your LlmAgent instance. The agent will then identify which tool to use based on your natural language prompts.

Vinkius manages your connection via an ephemeral sandbox. Your sensitive contact lists stay within the encrypted scope of your single endpoint token.

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