How to Use the Knoema MCP in LlamaIndex

Q: How can I use LlamaIndex to query Knoema time-series data I've already fetched?

When you get data using getdataseries, ingest it into a VectorStoreIndex. This makes the time-series data and its metadata searchable. Your agent can then query this index to find trends without making another API call.

Q: Can a LlamaIndex agent search Knoema for specific economic indicators?

Yes. You equip the agent with the Knoema tools, including searchdataseries. When asked a question it can't answer from its index, it will use that tool to actively search Knoema's entire catalog for the relevant indicator.

Q: What's the process for connecting my LlamaIndex app to the Knoema MCP Server?

It's a few lines of code. You instantiate the BasicMCPClient with the server URL, wrap it in the McpToolSpec, and then call totoollistasync(). The resulting tools can be passed directly to your LlamaIndex agent.

Q: Does this work with different LlamaIndex index types?

Absolutely. You can feed the output from Knoema tools like getdatasetmetadata into a SummaryIndex for high-level summaries or raw getdataseries output into a VectorStoreIndex for semantic search on specific data points.

Index Knoema's economic data with LlamaIndex. Build RAG agents that answer questions with real, verifiable numbers.

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Connect Knoema MCP to LlamaIndex

Create your Vinkius account to connect Knoema 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 Knoema with LlamaIndex

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Turn API Calls into a Knowledge Base

LlamaIndex doesn't just use the Knoema tools; it remembers what they return. When your agent calls `get_data_series` to pull historical GDP data, LlamaIndex can automatically index that output into a vector store. It turns transient API responses into a persistent, searchable knowledge base. Now, when you ask a follow-up question, your agent can query its own index first. You save on redundant API calls and get faster answers because the agent is querying data it already has on hand.

Ground Your Agent in Live Economic Data

This is how you stop hallucinations. A LlamaIndex agent can answer "What's the latest unemployment rate?" by first checking its index. If the data is stale, it knows to trigger a live tool call to `get_latest_dataset_data` from the Knoema MCP Server. After getting the fresh data point, it updates its index and then formulates the answer. This is Retrieval-Augmented Generation using live Knoema data, ensuring your agent's responses are always based on the most current statistics available.

Build a Data-Aware LlamaIndex Agent

Setting this up is straightforward. You use the `McpToolSpec` to give a `FunctionAgent` access to the Knoema toolset. From there, the agent intelligently decides its own strategy for each query it receives. It might use `search_datasets` to discover a totally new data source, or it might decide the answer is already in its vector index from a previous session. It combines your existing knowledge with the ability to fetch new data on the fly.

Setup guide

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

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Common questions about Knoema MCP in LlamaIndex

When you get data using `get_data_series`, ingest it into a `VectorStoreIndex`. This makes the time-series data and its metadata searchable. Your agent can then query this index to find trends without making another API call.

Yes. You equip the agent with the Knoema tools, including `search_data_series`. When asked a question it can't answer from its index, it will use that tool to actively search Knoema's entire catalog for the relevant indicator.

It's a few lines of code. You instantiate the `BasicMCPClient` with the server URL, wrap it in the `McpToolSpec`, and then call `to_tool_list_async()`. The resulting tools can be passed directly to your LlamaIndex agent.

Absolutely. You can feed the output from Knoema tools like `get_dataset_metadata` into a `SummaryIndex` for high-level summaries or raw `get_data_series` output into a `VectorStoreIndex` for semantic search on specific data points.

The indexing happens in your environment, not on our servers. The Knoema MCP server just responds to direct, stateless requests for economic statistics and metadata. It has no visibility into your LlamaIndex knowledge base or how you use the data.

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