Where do I get my Knoema API credentials?

Visit the **Knoema Developer Portal** (knoema.com/dev), create an application, and you will receive a Client ID and Client Secret.

How can I find a specific dataset ID?

Use the `search_datasets` tool with relevant keywords. The tool will return a list of matching datasets along with their unique IDs.

What is a mnemonic in Knoema?

A mnemonic is a short, human-readable code used to identify a specific data series within a dataset (e.g., 'NGDP' for Nominal GDP).

Knoema MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: Knoema
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Knoema through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Knoema "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Knoema?"
    )
    print(result.data)

asyncio.run(main())

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* 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.

Pydantic AI validates every Knoema tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

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 Pydantic AI 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 Pydantic AI via MCP

Follow these steps to integrate the Knoema MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

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 with type-safe schemas

Why Use Pydantic AI with the Knoema MCP Server

Pydantic AI provides unique advantages when paired with Knoema through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Knoema integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Knoema connection logic from agent behavior for testable, maintainable code

Knoema + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Knoema MCP Server delivers measurable value.

Type-safe data pipelines: query Knoema with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Knoema tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Knoema and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Knoema responses and write comprehensive agent tests

Knoema MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Knoema to Pydantic AI 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 Pydantic AI

Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI

Common issues when connecting Knoema to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Knoema + Pydantic AI FAQ

Common questions about integrating Knoema MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Knoema MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.