How to Use the ECB Monetary — Money Supply, Yield Curves & Banknotes MCP in LlamaIndex

Q: How do I configure the ECB Monetary — Money Supply, Yield Curves & Banknotes server in LlamaIndex?

Install llama-index-tools-mcp. Set up a BasicMCPClient, wrap it with McpToolSpec, and pass the async tool list to your FunctionAgent.

Q: Can I filter which ECB tools the agent uses?

Yes. You can use the allowedtools parameter to restrict the agent. If you only want it checking the yield curve, you just block the money supply and banknote functions.

Index live ECB yield curves and M3 monetary data directly into your LlamaIndex RAG applications.

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Connect ECB Monetary — Money Supply, Yield Curves & Banknotes MCP to LlamaIndex

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Setup ECB Monetary — Money Supply, Yield Curves & Banknotes with LlamaIndex

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Ground Semantic Search in Live M3 Data

Static PDFs about monetary policy get outdated the moment the ECB releases new data. Your LlamaIndex application needs live figures to ground its answers. It calls `get_monetary_aggregate` to pull the latest M1, M2, and M3 statistics. The framework indexes this raw JSON output into your vector store. When a user asks about inflation pressures, the query engine searches your embedded documents alongside the live M3 data. You get answers based on actual central bank numbers, not outdated training weights.

Query Yield Curves via MCP Server

You want to ask your data if the bond market expects a recession. The agent runs `get_yield_curve_snapshot` to capture the exact shape of the eurozone government bond market. This snapshot is ingested as a searchable document. Your LlamaIndex router evaluates the user's question, retrieves the inverted yield curve data from the index, and synthesizes a response comparing current 3-month and 10-year rates. The connection is stateless, but your index retains the historical context.

Index Physical Banknote Circulation

Tracking physical cash demand helps identify shadow economy shifts. Your application fetches monthly circulation totals for €5 to €500 denominations using `get_banknotes`. It pairs this with long-term bond rates retrieved through `get_yield_curve`. The `FunctionAgent` combines these outputs, allowing your users to run semantic queries against a unified knowledge base of physical liquidity and sovereign debt yields.

Setup guide

Set up ECB Monetary — Money Supply, Yield Curves & Banknotes 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 ECB Monetary — Money Supply, Yield Curves & Banknotes 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 ECB Monetary — Money Supply, Yield Curves & Banknotes tools.",
)
response = await agent.run("List recent ECB Monetary — Money Supply, Yield Curves & Banknotes data")

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Common questions about ECB Monetary — Money Supply, Yield Curves & Banknotes MCP in LlamaIndex

Install `llama-index-tools-mcp`. Set up a `BasicMCPClient`, wrap it with `McpToolSpec`, and pass the async tool list to your `FunctionAgent`.

Yes. The framework takes the raw central bank data and embeds it into your vector store. This turns ephemeral API responses into permanent, searchable knowledge.

RAG usually relies on static text. By connecting this server, your semantic search includes the absolute latest eurozone liquidity and bond market figures straight from the source.

Yes. You can use the `allowed_tools` parameter to restrict the agent. If you only want it checking the yield curve, you just block the money supply and banknote functions.

Never. The server strictly returns public eurozone macroeconomic indicators like M3 supply and government bond yields. The zero-trust V8 sandbox ensures the endpoint has absolutely zero access to your embeddings, documents, or index configurations.

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