Knoema MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Knoema as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Knoema. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Knoema?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Knoema MCP Server
Connect your AI agent to Knoema, the most comprehensive source of global decision-making data.
LlamaIndex agents combine Knoema tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Dataset Discovery — Search through millions of datasets from official sources like IMF, World Bank, and UN
- Data Retrieval — Fetch precise time-series data using mnemonics for your analysis and forecasting
- Metadata Auditing — Get detailed information about data sources, units, and frequencies
- Granular Search — Find specific indicators (e.g., GDP, CPI, Crude Oil Price) across multiple providers
- Visualization Support — Access atlas and dashboard resources for visual data context
Use Cases
- Economic Analysis — gather historical and current macro indicators for market research
- Business Planning — use demographic and sector data to inform strategy
- Scientific Research — find environmental and social datasets for academic or professional studies
The Knoema MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Knoema to LlamaIndex via MCP
Follow these steps to integrate the Knoema MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from Knoema
Why Use LlamaIndex with the Knoema MCP Server
LlamaIndex provides unique advantages when paired with Knoema through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Knoema tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Knoema tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Knoema, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Knoema tools were called, what data was returned, and how it influenced the final answer
Knoema + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Knoema MCP Server delivers measurable value.
Hybrid search: combine Knoema real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Knoema to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Knoema for fresh data
Analytical workflows: chain Knoema queries with LlamaIndex's data connectors to build multi-source analytical reports
Knoema MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Knoema to LlamaIndex via MCP:
get_data_series
Requires dataset ID and a list of mnemonics. Get specific data series
get_dataset_metadata
Critical for understanding what variables are available. Get metadata for a specific dataset
get_knoema_resource
Get a generic frontend resource
get_latest_dataset_data
Get the most recent data points for a dataset
list_data_frequencies
g., Annual, Quarterly, Monthly). List available data frequencies
list_data_topics
g., Agriculture, Economy, Demographics). List all available data topics in Knoema
list_data_units
g., Percentage, USD, Kilograms). List measurement units
list_dataset_regions
) supported by a specific dataset. List regions available in a dataset
search_data_series
More granular than dataset search. Ideal for finding specific indicators. Search for specific data series across all datasets
search_datasets
Returns dataset IDs and metadata. Use this to find the correct data source for your statistics. Search for datasets in Knoema
Example Prompts for Knoema in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Knoema immediately.
"Search for datasets about renewable energy in Europe"
"Get the metadata for dataset 'IMFWEOS2024Oct'"
"Search for crude oil price series"
Troubleshooting Knoema MCP Server with LlamaIndex
Common issues when connecting Knoema to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpKnoema + LlamaIndex FAQ
Common questions about integrating Knoema MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Knoema with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Knoema to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.