How to Use the Chatham Financial MCP in CrewAI

Deploy an autonomous CrewAI risk management team to monitor Chatham Financial trades and market data.

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Connect Chatham Financial MCP to CrewAI

Create your Vinkius account to connect Chatham Financial to CrewAI 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 CrewAI

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Autonomous Market Research

`get_chatham_market_data` and `get_trade_valuations` equip your quantitative analyst agent with live benchmark rates and historical pricing using this MCP Server. You assign these specific endpoints to a researcher role, isolating data gathering from decision making. The researcher pulls the forward curves and writes the metrics into shared memory. A secondary risk manager agent reads those numbers, compares them against internal thresholds, and decides if portfolio rebalancing is necessary.

CrewAI MCP Server Integration for Chatham Financial

`list_chatham_transactions` and `list_trade_payments` give your operations agents visibility into settlement schedules and financial trades. Passing these tools via the connection array lets a dedicated monitoring agent track upcoming cash flows across all legal entities. We design multi-agent setups to run sequentially for this exact reason. The monitor flags an anomalous payment, and a separate moderator agent escalates the transaction details to a human controller without interrupting the main loop.

Role-Based Accounting Access

`get_trade_accounting` and `get_hedge_effectiveness` supply the compliance documentation your audit agents require. You use the HTTP server class with a filter to ensure only the auditor role can access hedge accounting tests. Restricting access prevents your general research agents from accidentally querying sensitive effectiveness results. The crew operates hierarchically, meaning a manager agent coordinates which subordinate gets to pull portfolio data using `list_chatham_portfolios`.

Setup guide

Set up Chatham Financial MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Chatham Financial tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Chatham Financial Analyst",
    goal="Access and analyze Chatham Financial data via MCP.",
    backstory="Expert analyst with direct Chatham Financial access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Chatham Financial transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Chatham Financial Analyst",
        goal="Access and analyze Chatham Financial data via MCP.",
        backstory="Expert analyst with direct Chatham Financial access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Chatham Financial transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

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 CrewAI

Pass your Vinkius MCP Server endpoint directly into the connection array when defining your agent. Install the tools package to enable the underlying protocol support. The framework handles the HTTP transport negotiation automatically.

You configure a dedicated compliance agent with the specific hedge testing tools. This agent runs on a schedule, fetching the latest test results and writing summaries to the crew's shared memory for other agents to review.

Import the HTTP server class from the protocol package and instantiate it with a tool filter. You define exactly which endpoints an agent can see, keeping payment schedules isolated from market data researchers.

Agents execute tasks sequentially or hierarchically based on your crew configuration. A watcher agent loops through the forward curve requests, triggering downstream analysis tasks only when the benchmark rates change.

Your managed portfolio identifiers are processed within a hardened, ephemeral V8 isolate. Vinkius guarantees zero-trust execution by destroying the environment immediately after the API returns the transaction list. No state persists across agent sessions.

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