How to Use the Chatham Financial MCP in Pydantic AI

Q: How do I configure Chatham Financial with Pydantic AI?

Install pydantic-ai-slimmcp first. Define an MCPToolset pointing to your Vinkius HTTP endpoint, then pass it into your Agent's toolsets parameter. Remember that MCPServerHTTP is deprecated.

Enforce strict type safety on Chatham Financial data with Pydantic AI. Catch validation errors before they break your system.

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Connect Chatham Financial MCP to Pydantic AI

Create your Vinkius account to connect Chatham Financial to Pydantic AI 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 Pydantic AI

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Claude

Perplexity

Validate market data with your MCP Server

The `get_chatham_market_data` tool fetches benchmark rates, and Pydantic AI ensures every float and date matches your exact schema. If the API returns a string instead of a decimal, your agent fails loudly. You apply the same strict checks to `get_trade_valuations`. Historical trade pricing must be perfectly structured for financial reporting. This framework guarantees your agent never hallucinates a missing valuation field.

Guarantee accurate hedge accounting

The `get_trade_accounting` tool pulls transaction details, letting your agent parse the response against defined Pydantic models at runtime. Financial compliance leaves zero room for silent data corruption. Next, it runs `get_hedge_effectiveness` to verify test results. Because this MCP Server is model-agnostic, you can swap between Claude or OpenAI while maintaining the exact same data validation guarantees.

Map payment schedules safely

The `list_chatham_entities` tool pulls your legal structures, and runtime validation ensures no account IDs get truncated or malformed. Corporate treasury requires precise entity mapping. Your agent then calls `list_trade_payments` to build out the cash flow schedule. If a payment date format shifts unexpectedly, the execution stops immediately. You fix the issue instead of processing bad numbers.

Setup guide

Set up Chatham Financial MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "chatham-financial-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Chatham Financial tools.",
)

result = await agent.run("List recent Chatham Financial transactions")
print(result.output)

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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 Pydantic AI

Install pydantic-ai-slim[mcp] first. Define an MCPToolset pointing to your Vinkius HTTP endpoint, then pass it into your Agent's toolsets parameter. Remember that MCPServerHTTP is deprecated.

Financial applications demand correctness. This framework validates responses from tools like list_chatham_transactions against strict types, preventing silent errors in your trade logic.

Yes. The framework is completely model-agnostic. You can query your financial portfolios via the MCP standard using local models, OpenAI, or Anthropic.

No. Vinkius hosts the infrastructure. You just point your toolset to the provided remote URL and start querying.

We secure your corporate entity lists using strict zero-trust protocols. The connection requires one endpoint token, and the execution environment spins down immediately after the response validates.

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