How to Use the Chatham Financial MCP in LlamaIndex

Q: Can I filter tools for LlamaIndex?

Yes, you can use the allowedtools filter to limit which functions the agent can access. This keeps your index focused on the data you care about.

Index your hedge effectiveness and trade data into LlamaIndex for grounded, searchable financial knowledge.

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Connect Chatham Financial MCP to LlamaIndex

Create your Vinkius account to connect Chatham Financial to LlamaIndex 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 LlamaIndex

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Indexing Chatham Financial with LlamaIndex

Push results from `get_hedge_effectiveness` into a vector store to make your past test results queryable. You turn raw numbers into a searchable knowledge base. LlamaIndex stores these outputs so your RAG pipeline can reference previous performance. It creates a history of your hedge status that you can query in plain English.

Retrieve trade history for LlamaIndex

Call `list_trade_payments` to pull your entire payment schedule into your index. This gives your agent a complete timeline of your financial obligations. Your RAG application uses this index to answer questions about specific payment dates. It replaces static reports with a live, queryable database of your trades.

Semantic search for Chatham Financial

Use `list_chatham_entities` to map your legal structure inside your LlamaIndex knowledge base. Your agent understands exactly which entity owns which portfolio. This makes your search results context-aware. When you ask about a specific portfolio, the agent knows the legal entity context before it even starts looking.

Setup guide

Set up Chatham Financial MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Chatham Financial MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Chatham Financial tools.",
)
response = await agent.run("List recent Chatham Financial data")

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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 LlamaIndex

You use the MCP tool spec to pull the data and index it into your vector store. From there, your query engine can find answers across your trade records.

The server provides read-only access to your data. Your LlamaIndex application decides when to trigger a refresh of the index based on your requirements.

Access is restricted to your authorized endpoint token. No sensitive transaction details are cached globally; they only exist within your private vector index.

Yes, you can use the allowed_tools filter to limit which functions the agent can access. This keeps your index focused on the data you care about.

Only if you explicitly add them to your index. The server transmits the payment data, and your application stores it in your private memory space.

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