How to Use the Writer (AI Enterprise LLM) MCP in Google ADK

Q: Can I manage multiple large documents in Writer (AI Enterprise LLM) and Google ADK?

Absolutely. You can use uploadfile for each document, and then the system maintains a record of them via listfiles. The agent keeps track of all this content when it builds or updates Knowledge Graphs using addfiletograph.

Q: How does Writer (AI Enterprise LLM) handle text translation within Google ADK?

The dedicated translatetext tool handles this. Your agent sends the source text and the target language, and the server returns the translated version in a clean, usable format.

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Connect Writer (AI Enterprise LLM) MCP to Google ADK

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Setup Writer (AI Enterprise LLM) with Google ADK

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Deep Data Querying

The `ask_question` tool lets your agent query structured Knowledge Graphs, pulling answers directly from your enterprise data. You don't just get a search result; you get an answer derived from the graph structure. If the source material is unstructured files, remember to use `upload_file` first. Then, the agent can ingest that data and let you run complex queries using `list_graphs` to see what boundaries exist.

Building Applications

Need more than just text? You can spin up a no-code application workflow. Your agent manages this with `generate_application_content_async`, which returns a `job_id`. Later, you check the status using `get_application_job` or retry it if something goes wrong via `retry_application_job`. This process is fully managed through tools like `list_applications` and `get_application`, making complex workflows reliable.

Multi-Modal Analysis

Writer (AI Enterprise LLM) handles both text and vision. You can use `analyze_vision` to process images or documents, extracting meaning using a prompt. If the source document is a PDF, the `parse_pdf` tool helps get it into a machine-readable format. For keeping data fresh, your agent should always check current events with `web_search`, which brings external context into the system.

Setup guide

Set up Writer (AI Enterprise LLM) 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 Writer (AI Enterprise LLM) 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="Writer (AI Enterprise LLM)_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Writer (AI Enterprise LLM) tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Writer (AI Enterprise LLM) MCP in Google ADK

The agent uses its native integration points. You can write complex reasoning flows that start with a prompt, get context via `web_search`, then structure the data using `create_graph`, and finally answer questions with `ask_question`.

Absolutely. You can use `upload_file` for each document, and then the system maintains a record of them via `list_files`. The agent keeps track of all this content when it builds or updates Knowledge Graphs using `add_file_to_graph`.

The agent can monitor the status. If a failure occurs, you don't need to start over; simply call `retry_application_job` using the job ID retrieved from `get_application_job`. This keeps your pipelines moving.

The dedicated `translate_text` tool handles this. Your agent sends the source text and the target language, and the server returns the translated version in a clean, usable format.

The core data types are file metadata, uploaded documents (binary content), structured text within Knowledge Graphs, and raw query inputs.

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