How to Use the Celoxis MCP in Google ADK

Q: Can I filter which tools my agent sees?

Yes. You supply a toolnames list when configuring the toolset. This restricts the agent to specific operations like listresources while hiding financial tools.

Feed Celoxis portfolio data directly into Google ADK via MCP and analyze massive project timelines using Gemini's million-token context.

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Connect Celoxis MCP to Google ADK

Create your Vinkius account to connect Celoxis 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 Celoxis with Google ADK

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Ingest massive WBS structures

The `list_tasks` and `list_milestones` tools dump your entire Work Breakdown Structure straight into Gemini's context window. The agent reads thousands of concrete deliverables and absolute phase tracking markers simultaneously. You don't have to chunk project data anymore. The model holds the entire project history in memory, letting your enterprise agents spot delivery patterns across hundreds of concurrent phases.

Cross-reference Celoxis with BigQuery

Your agent pulls top-level organizational data using `list_clients` and `list_portfolios`. It maps these strategic global tracking items against internal financial records you already keep in Google Cloud. This setup bridges ERP operations with external datasets. The agent grabs physical IDs from Celoxis and runs SQL queries against your BigQuery tables in the exact same reasoning step.

Analyze risks with the Google ADK MCP Server

The `list_risks` and `list_issues` tools expose custom application matrices representing blocked workflows. Your system reads these explicit organizational constraints to figure out why projects stall. It combines this bottleneck data with financial realities pulled via `list_expenses`. The agent evaluates raw billable items alongside blocked issues to calculate total financial exposure.

Setup guide

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

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Common questions about Celoxis MCP in Google ADK

Install google-adk via pip. Wrap the endpoint in McpToolset using StreamableHttpServerParameters and pass it to your LlmAgent tools array.

Yes. You supply a tool_names list when configuring the toolset. This restricts the agent to specific operations like list_resources while hiding financial tools.

Gemini models handle over a million tokens natively. Your agent can read the output of get_project across dozens of massive portfolios without losing track of early details.

Your agent executes MCP tools to pull project statuses, then passes those raw strings into Vertex AI prediction endpoints. The framework handles the transport layer automatically.

Vinkius operates a zero-trust ephemeral environment. Your timesheets, blocked issue logs, and client portfolio mappings process entirely in memory and vanish the millisecond the connection drops.

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