How to Use the FRED Tags & Sources — Data Discovery MCP in CrewAI

Equip your CrewAI agent teams with direct access to FRED tags and economic sources.

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Connect FRED Tags & Sources — Data Discovery MCP to CrewAI

Create your Vinkius account to connect FRED Tags & Sources — Data Discovery 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 FRED Tags & Sources — Data Discovery with CrewAI

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Collaborative tag discovery for CrewAI agents

The `search_tags` tool locates specific classification labels across FRED's massive database. Your research agent uses this tool to find relevant tags, sharing the discovered terms with other agents in the crew. This collaborative approach allows a specialized analyst agent to take those tags and immediately begin deeper economic modeling. No manual keyword mapping is required to keep the team aligned.

Multi-agent series isolation via MCP Server tools

The `get_series_by_tags` tool filters economic series by intersecting multiple tag parameters like region, frequency, and topic. A data-gathering agent runs this tool to compile a highly specific list of series IDs. Once compiled, the list is passed to an analyst agent through CrewAI's shared memory. This keeps your automated research pipelines focused only on the exact economic indicators you specify.

Map data origins across 107 official sources

The `list_sources` tool pulls the complete directory of official economic data providers, including the BLS and Census Bureau. A moderator agent uses this tool to verify that your crew only consumes data from approved institutional sources. This step ensures strict data governance within your autonomous team. You can prevent agents from using obscure or unverified data sources during their analytical runs.

Setup guide

Set up FRED Tags & Sources — Data Discovery 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 FRED Tags & Sources — Data Discovery tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent FRED Tags & Sources — Data Discovery 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="FRED Tags & Sources — Data Discovery Analyst",
        goal="Access and analyze FRED Tags & Sources — Data Discovery data via MCP.",
        backstory="Expert analyst with direct FRED Tags & Sources — Data Discovery access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent FRED Tags & Sources — Data Discovery 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 FRED. 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 FRED Tags & Sources — Data Discovery MCP in CrewAI

Pass the server URL directly into the agent's mcps configuration array during initialization. CrewAI automatically discovers and exposes the tools from this MCP server to that specific agent.

Yes, CrewAI supports parallel tool execution across different agents. Your research agent can search tags while your validation agent checks the active list of data sources.

Agents pass the retrieved tag names and series IDs through CrewAI's shared memory or task outputs. This allows downstream agents to immediately use the filtered data for complex analysis.

Both execution styles are supported. In a hierarchical setup, a manager agent assigns tag discovery tasks to a researcher before ordering an analyst to process the resulting series.

Your API key is managed securely as an environment variable in the Vinkius hosting environment. The CrewAI framework only interacts with the secure MCP endpoint, keeping your credentials completely hidden from the agent execution logs.

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