How to Use the FRED Full Access — U.S. Economic Intelligence MCP in CrewAI

Q: How do I divide tasks between agents when using FRED Full Access — U.S. Economic Intelligence with CrewAI?

Assign the search tools like searchseries to a specialized researcher agent, and the data retrieval tools like getobservations to an analyst agent. CrewAI coordinates the handoff, ensuring the analyst receives the correct series IDs to pull the raw numbers.

Q: Can a CrewAI agent monitor the economic release calendar using FRED Full Access — U.S. Economic Intelligence?

Yes, a dedicated monitor agent runs getreleasedates to track upcoming data releases. When a scheduled release occurs, the monitor agent triggers the rest of the crew to fetch and analyze the new data.

Q: How do I expose specific tools from the FRED Full Access — U.S. Economic Intelligence MCP Server to my CrewAI agents?

Use the MCPServerHTTP class from crewai.mcp and apply a toolfilter to expose only the necessary tools, like getseries or getcategory. This prevents your agents from wasting tokens on tools they do not need.

Q: How do agents handle geographic economic data?

The mapping agent calls getgeoshapes with specific parameters like state or county. It then matches these shapes with data from getregionaldata to build localized economic profiles for the crew.

Deploy specialized agent teams to analyze Federal Reserve data using CrewAI and FRED Full Access — U.S. Economic Intelligence.

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Setup FRED Full Access — U.S. Economic Intelligence with CrewAI

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Multi-Agent Economic Analysis Teams

Your research agent uses `search_series` and `get_series_by_tags` to discover relevant macroeconomic indicators, while your analyst agent processes the raw numbers. CrewAI coordinates this multi-agent team, passing findings from one specialist to the next. The research agent passes specific series IDs like DGS10 or FEDFUNDS to the analyst agent. Analyst agents then run `get_observations` to calculate yield spreads and build reports autonomously.

Regional Economic Mapping via MCP Server

We use `get_geo_shapes` and `get_regional_data` to feed geographic shapefiles to a specialized mapping agent. The agent parses regional differences across states or counties, preparing structured reports for your investment crew. This MCP Server provides the raw geographic data directly to the agent's memory. CrewAI's shared memory allows other agents in the crew to immediately use these regional insights for localized market analysis.

Automated Vintage and Revision Auditing

Our auditing crew calls `get_vintage_dates` and `get_release_series` to check how past economic reports were revised over time. A moderator agent oversees this process, ensuring the analysis agent does not use future data during historical backtesting. By isolating the tasks, your crew avoids look-ahead bias in financial models. The sequential execution model ensures each step of the revision audit completes before the final report compiles.

Setup guide

Set up FRED Full Access — U.S. Economic Intelligence 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 Full Access — U.S. Economic Intelligence tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent FRED Full Access — U.S. Economic Intelligence 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 Full Access — U.S. Economic Intelligence Analyst",
        goal="Access and analyze FRED Full Access — U.S. Economic Intelligence data via MCP.",
        backstory="Expert analyst with direct FRED Full Access — U.S. Economic Intelligence access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent FRED Full Access — U.S. Economic Intelligence 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 Full Access — U.S. Economic Intelligence MCP in CrewAI

Assign the search tools like `search_series` to a specialized researcher agent, and the data retrieval tools like `get_observations` to an analyst agent. CrewAI coordinates the handoff, ensuring the analyst receives the correct series IDs to pull the raw numbers.

Yes, a dedicated monitor agent runs `get_release_dates` to track upcoming data releases. When a scheduled release occurs, the monitor agent triggers the rest of the crew to fetch and analyze the new data.

Use the `MCPServerHTTP` class from `crewai.mcp` and apply a `tool_filter` to expose only the necessary tools, like `get_series` or `get_category`. This prevents your agents from wasting tokens on tools they do not need.

The mapping agent calls `get_geo_shapes` with specific parameters like state or county. It then matches these shapes with data from `get_regional_data` to build localized economic profiles for the crew.

Yes, all queries for geographic shape files and time-series data are processed through secure, single-use V8 isolates running the MCP. Vinkius guarantees that your search queries and analytical workflows are strictly private and never used to train models.

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