How to Use the Mambu MCP in LlamaIndex

Q: How do I index Mambu loan portfolios into LlamaIndex?

You run a retrieval loop using listloanaccounts to fetch active loan data, then convert the JSON payloads into LlamaIndex Document objects. These documents are embedded and stored in your vector database. This lets you run semantic searches over your entire core Mambu banking portfolio to find specific risk indicators or payment terms.

Q: Can LlamaIndex query Mambu transaction histories using natural language?

Yes, by exposing listtransactions as an engine tool, LlamaIndex translates natural queries into API parameters. The engine calls the tool, retrieves the transaction records, and synthesizes a natural response grounded in the exact Mambu ledger entries. This avoids the need to write complex SQL or REST queries to verify customer spending habits.

Q: How does the Mambu MCP tool setup handle document chunking in LlamaIndex?

The tool outputs from getclient are structured JSON strings, which you pass to LlamaIndex's JSON parser node. The parser splits the customer data by key-value pairs rather than arbitrary character counts. This preserves the relationships between client identifiers, deposit balances, and active loan structures during indexing.

Q: What happens if an account search returns no records?

If getdepositaccount returns an empty payload or error, LlamaIndex handles the exception through its standard routing logic. The query engine can fall back to listdepositaccounts with a search filter to locate the correct account ID. This fallback mechanism ensures your financial search tools do not crash on minor input typos.

Index live Mambu banking records directly into LlamaIndex vector stores using our secure MCP server.

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Connect Mambu MCP to LlamaIndex

Create your Vinkius account to connect Mambu 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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Turn Mambu ledger logs into searchable LlamaIndex indices

`list_transactions` extracts historical Mambu ledger events to build a queryable LlamaIndex vector index of client financial activity. Your LlamaIndex pipeline ingests these raw Mambu transactions, converts them into document nodes, and updates your vector store in real time. This process removes reliance on stale database replicas for Mambu financial audits. When a user asks LlamaIndex about a specific payment pattern, the index retrieves the exact node generated from `list_deposit_accounts` to answer with absolute ledger accuracy.

Context-grounded customer lookups with LlamaIndex MCP Server

`get_client` retrieves the core identity and risk profile of a banking customer directly into your LlamaIndex retrieval-augmented generation pipeline. LlamaIndex uses this fresh Mambu data to ground LLM responses, ensuring the model never fabricates account statuses or balances. By feeding the output of Mambu `list_loan_accounts` into the LlamaIndex query engine, you merge unstructured customer service notes with structured loan data. The resulting LlamaIndex search index provides a complete, factual view of the client's current Mambu liabilities.

Map administrative tasks to banking context

`list_tasks` pulls operational back-office assignments directly from your Mambu banking environment into the LlamaIndex knowledge base. Your LlamaIndex agent queries this index to find pending credit reviews, outstanding documentation requests, or urgent collection tasks. Combining Mambu task data with client records fetched via `get_task` allows the LlamaIndex engine to surface bottlenecks in your loan approval process. The index highlights which credit officers are assigned to delayed Mambu tasks, matching them against active accounts.

Setup guide

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

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Common questions about Mambu MCP in LlamaIndex

You run a retrieval loop using `list_loan_accounts` to fetch active loan data, then convert the JSON payloads into LlamaIndex Document objects. These documents are embedded and stored in your vector database. This lets you run semantic searches over your entire core Mambu banking portfolio to find specific risk indicators or payment terms.

Yes, by exposing `list_transactions` as an engine tool, LlamaIndex translates natural queries into API parameters. The engine calls the tool, retrieves the transaction records, and synthesizes a natural response grounded in the exact Mambu ledger entries. This avoids the need to write complex SQL or REST queries to verify customer spending habits.

The tool outputs from `get_client` are structured JSON strings, which you pass to LlamaIndex's JSON parser node. The parser splits the customer data by key-value pairs rather than arbitrary character counts. This preserves the relationships between client identifiers, deposit balances, and active loan structures during indexing.

If `get_deposit_account` returns an empty payload or error, LlamaIndex handles the exception through its standard routing logic. The query engine can fall back to `list_deposit_accounts` with a search filter to locate the correct account ID. This fallback mechanism ensures your financial search tools do not crash on minor input typos.

Your core banking data stays in your secure ledger. The MCP server acts as an ephemeral bridge, passing Mambu client details and loan balances to LlamaIndex only during active query execution. All data processed in the V8 sandbox is immediately destroyed upon session closure, keeping your sensitive financial records completely isolated from persistent external storage.

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