How to Use the Chatham Financial MCP in Google ADK

Q: Can I restrict which tools my agent sees?

You sure can. The McpToolset accepts a toolnames filter, letting you expose just listtradepayments while hiding broader portfolio tools from specific agents.

Ground your Google ADK agents in real Chatham Financial risk data. Feed valuations straight into BigQuery.

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Connect Chatham Financial MCP to Google ADK

Create your Vinkius account to connect Chatham Financial 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 Chatham Financial with Google ADK

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Feed Chatham Financial MCP Server data

The `list_chatham_transactions` tool extracts your financial trades and feeds them into Gemini's massive context window. You can load thousands of transaction records at once without hitting token limits. Your agent can then use `list_chatham_portfolios` to group those trades by managed accounts. This raw data flows perfectly into Vertex AI pipelines for deeper enterprise analysis.

Analyze long-term forward curves

The `get_chatham_market_data` tool pulls the exact benchmark rates your treasury team uses to evaluate risk. Enterprise models need accurate market baselines from your MCP Server. Once you have the curves, your agent runs `get_trade_valuations` to pull historical pricing. You can dump these time-series arrays straight into BigQuery to join against your internal corporate datasets.

Audit hedge performance automatically

The `get_hedge_effectiveness` tool grabs the pass/fail metrics for your active hedges across any time period. Manual risk audits waste valuable engineering hours. If a specific hedge raises questions, the agent hits `get_trade_accounting` for the granular transaction details. You get complete visibility into your accounting designations directly within your Google Cloud environment.

Setup guide

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

Run pip install google-adk in your environment. Create a McpToolset using StreamableHttpServerParameters with your Vinkius URL, then pass it to your LlmAgent tools array.

You sure can. The McpToolset accepts a tool_names filter, letting you expose just list_trade_payments while hiding broader portfolio tools from specific agents.

Gemini processes the benchmark rates returned by the MCP standard natively. Its long-context reasoning lets you analyze years of forward curves in a single prompt.

It plugs right in. Agents built with this framework can pull financial data and immediately write the results to BigQuery or Cloud Storage.

Your hedge effectiveness test results pass through ephemeral, zero-trust infrastructure. Vinkius handles the authentication via a single endpoint token and leaves zero traces of your financial positions on our servers.

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