How to Use the ECB Exchange Rates — Official EUR Reference Rates MCP in LlamaIndex

Ground your LlamaIndex knowledge base in real ECB reference rates.

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Connect ECB Exchange Rates — Official EUR Reference Rates MCP to LlamaIndex

Create your Vinkius account to connect ECB Exchange Rates — Official EUR Reference Rates 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 ECB Exchange Rates — Official EUR Reference Rates with LlamaIndex

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Index daily exchange rates

Use `get_latest_rates` to pull the current ECB feed and feed it into your LlamaIndex vector store. This makes the data semantically searchable for your retrieval applications. Your RAG pipeline can now answer questions about historical or current currency positioning. It uses the actual API data as the source of truth.

Query specific currency pairs

Call `get_exchange_rate` to retrieve a single point of data for your index. This is useful when you need to ground an answer in a specific EUR cross-rate. The tool output becomes a document in your index. You can query it alongside your private knowledge files to get context-aware insights.

Compare multiple currencies semantically

Fetch a group of rates with `get_multiple_rates` and store them as a structured node in LlamaIndex. You can then query the relationships between these currencies. The agent uses these indexed nodes to provide grounded answers. It relies on the ECB data rather than internal model training.

Setup guide

Set up ECB Exchange Rates — Official EUR Reference Rates 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 Exchange Rates — Official EUR Reference Rates 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 Exchange Rates — Official EUR Reference Rates tools.",
)
response = await agent.run("List recent ECB Exchange Rates — Official EUR Reference Rates data")

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Common questions about ECB Exchange Rates — Official EUR Reference Rates MCP in LlamaIndex

The server returns structured JSON which LlamaIndex converts into nodes. You then add these nodes to your vector store for semantic retrieval.

Yes. By indexing the results of your tool calls, you build a searchable history of the rates you have retrieved over time.

It is. You can inject the tool outputs into your current index without changing your underlying data structure.

This server only performs read operations on public ECB data. It does not store or transmit any of your private indexed content.

Yes. The agent evaluates the user query and calls the appropriate tool if the information is missing from the current index.

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