How to Use the Knoema MCP in CrewAI

Q: How do I add Knoema tools to a CrewAI agent?

Pass the MCP Server URL directly into the mcps array when defining your agent. CrewAI handles the connection and exposes the tools automatically.

Q: Can I restrict which Knoema tools my CrewAI agents use?

Yes. Use MCPServerHTTP and apply a toolfilter. This lets you give searchdatasets to a researcher while restricting getdataseries to your data extraction agent.

Q: How do multiple CrewAI agents share Knoema data?

The framework uses shared memory. When one agent runs getdatasetmetadata, the resulting schema is stored in memory so the next agent knows exactly how to query the time-series.

Deploy autonomous researcher agents to track global statistics with this Knoema integration for CrewAI.

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Create your Vinkius account to connect Knoema to CrewAI 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 CrewAI

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Deploy CrewAI Economic Researchers

The `search_datasets` tool gives your CrewAI researcher agent the ability to hunt down exact statistical sources based on a vague prompt. You assign this tool to a scout agent whose only job is finding the right database for the task. Once the scout finds the source, it passes the dataset ID to an analyst agent equipped with `get_dataset_metadata`. The analyst reads the structure, understands the available dimensions, and plans the exact query needed for the final report.

Extract Time-Series Automatically

The `get_data_series` tool lets your execution agent pull raw historical data using specific mnemonics. Instead of a human downloading CSVs, your crew queries the exact timeline and variables required for their analysis via the MCP Server. If the team just needs a quick daily update, they hit `get_latest_dataset_data`. A moderator agent can watch this process, ensuring the data pulled matches the original request before passing it down the pipeline.

Standardize Global Statistics

The `list_data_frequencies` and `list_data_units` tools help your formatting agent standardize messy global data. Before writing the final output, the agent checks these endpoints to confirm whether the numbers represent monthly percentages or annual raw totals. The crew also uses `list_dataset_regions` to verify geographic coverage. If a user asks for European inflation data, the agent checks the region list first, avoiding failed queries by knowing exactly which countries are supported by the dataset.

Setup guide

Set up Knoema MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Knoema tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Knoema Analyst",
    goal="Access and analyze Knoema data via MCP.",
    backstory="Expert analyst with direct Knoema access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Knoema transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Knoema Analyst",
        goal="Access and analyze Knoema data via MCP.",
        backstory="Expert analyst with direct Knoema access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Knoema transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Knoema. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Knoema MCP in CrewAI

Pass the MCP Server URL directly into the `mcps` array when defining your agent. CrewAI handles the connection and exposes the tools automatically.

Yes. Use `MCPServerHTTP` and apply a `tool_filter`. This lets you give `search_datasets` to a researcher while restricting `get_data_series` to your data extraction agent.

The framework uses shared memory. When one agent runs `get_dataset_metadata`, the resulting schema is stored in memory so the next agent knows exactly how to query the time-series.

Yes, the framework supports stdio and SSE transports. You can watch your crew execute searches and pull macroeconomic figures live in your CLI.

The MCP Server handles global statistics and macroeconomic time-series data exclusively. Every agent request runs in an ephemeral, zero-trust environment that drops all context the moment the connection closes.

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