How to Use the Griffin MCP in LlamaIndex

Q: Can I search Griffin records in LlamaIndex?

Yes. Once you run listtransactions, the data becomes part of your knowledge base. You can then query that specific transaction history using semantic search.

Turn Griffin financial data into a searchable knowledge base using LlamaIndex for grounded, accurate AI responses.

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Connect Griffin MCP to LlamaIndex

Create your Vinkius account to connect Griffin 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 Griffin with LlamaIndex

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Perplexity

Index Griffin data for RAG

Convert your API results into vector embeddings for semantic search. When you run `list_verifications`, the output is indexed immediately for your agent to query later. Stop hallucinating about account history. Your AI client pulls from a grounded knowledge base that stays updated with the latest Griffin records.

Grounded financial decision making

Query your banking data using natural language instead of raw IDs. Your agent references indexed data from `get_organization` and `list_accounts` to answer complex questions. Your model understands the context of your ledger. It retrieves specific transaction details by searching the index rather than relying on stale training data.

Unified knowledge retrieval

Combine your private documents with live Griffin API data. Your RAG pipeline merges indexed reports and internal banking records for a complete view of your firm. Everything is searchable. You get a single interface to ask questions about your financial status, payees, and active verifications.

Setup guide

Set up Griffin 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 Griffin 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 Griffin tools.",
)
response = await agent.run("List recent Griffin data")

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Common questions about Griffin MCP in LlamaIndex

You connect the client and convert the tool output into a searchable vector store. This ensures your agent answers questions based on actual API data.

Yes. Once you run `list_transactions`, the data becomes part of your knowledge base. You can then query that specific transaction history using semantic search.

It does. You map the tool specifications to your agent, allowing it to perform actions like `get_account` while simultaneously searching your index.

It provides structured records including organization details, account balances, transaction logs, and KYC/KYB verification statuses.

The server uses ephemeral sessions for all data retrieval. Records like your `list_payees` or account info are never stored permanently by the server, ensuring your sensitive financial data remains private.

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