How to Use the Brokermint MCP in LlamaIndex

Turn Brokermint into a searchable knowledge base using LlamaIndex for grounded, data-driven answers.

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Connect Brokermint MCP to LlamaIndex

Create your Vinkius account to connect Brokermint 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 Brokermint with LlamaIndex

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Index Brokermint records in LlamaIndex

Run `list_contacts` to pull your entire database into a local index. LlamaIndex then converts these contacts into searchable, semantic vectors. Your agent answers questions about specific sellers or buyers based on real data. It stops relying on guesswork and starts querying your actual records.

Ground AI answers with Brokermint

Use `get_transaction` to feed live deal details into your RAG pipeline. The agent uses this as context for every response it generates. This approach forces the model to cite specific transaction IDs. You see exactly which file the AI used to form its conclusion.

Control tool access for LlamaIndex

Use the allowed_tools filter to limit which functions the agent can touch. You might restrict it to `get_account_info` while blocking write operations. This keeps your agent focused on querying. It prevents unauthorized modifications while letting you browse your back-office data freely.

Setup guide

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

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Common questions about Brokermint MCP in LlamaIndex

It calls the server tools to fetch data and then embeds those results into your vector index. You can then perform semantic searches across all your files.

Absolutely. By including resources, the agent treats your back-office data as a primary document source for answering user questions.

Yes, use the async tool list methods provided by the client. It ensures your data retrieval doesn't block other agent processing tasks.

Your contact names and details reside in an ephemeral memory space during the session. We clear this data immediately after your query terminates.

Every request is routed through a secure Vinkius sandbox. Your transaction data remains unreadable to any third party during transit.

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