How to Use the U.S. Congress MCP in CrewAI

Q: Can I get member details and nominations in one run using U.S. Congress and CrewAI?

You assign two specialized agents: a 'Member Agent' for listcongressmembers and a 'Nomination Agent' for listcongressnominations. They collaborate to build a comprehensive report.

Run autonomous Congressional research operations with CrewAI on the U.S. Congress MCP Server.

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Connect U.S. Congress MCP to CrewAI

Create your Vinkius account to connect U.S. Congress 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 U.S. Congress with CrewAI

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Autonomous Legislative Research Teams

Define a 'Research Agent' that uses `list_congress_bills` to get an initial list of bills, and a second 'Analysis Agent' that takes those IDs and calls `get_bill_details`. The crew runs these steps sequentially, passing memory between agents. The system monitors the full process. If the bill search yields too many results, the monitor agent can automatically flag them for human review.

Managing Congress Records and Personnel

You can build a crew where one agent specializes in personnel (using `list_congress_members` or `list_congress_nominations`), while another focuses on the legislative records. They work together to cross-reference which members were involved with specific nominations. The shared memory allows agents to reference results from previous steps, ensuring comprehensive coverage of both people and policies.

Historical Data Collection Pipelines

Set up a crew that first uses `list_congress_sessions` to determine the necessary date range. The next agent then takes those dates and iteratively calls tools like `list_congress_treaties` for each historical session. The whole operation is autonomous—it runs, collects data across multiple domains (dates, treaties), and reports back without needing manual intervention.

Setup guide

Set up U.S. Congress 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 U.S. Congress tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent U.S. Congress transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

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

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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="U.S. Congress Analyst",
        goal="Access and analyze U.S. Congress data via MCP.",
        backstory="Expert analyst with direct U.S. Congress access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent U.S. Congress 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 Congress.gov. 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 U.S. Congress MCP in CrewAI

Yes. You assign one agent the role of 'Committee Specialist.' This agent uses `list_congress_committees` to get all committees, then other agents use that data to analyze specific members.

You create a dedicated 'Detail Agent' whose sole job is calling `get_bill_details`. This agent acts on the initial list provided by another agent, ensuring deep data retrieval.

The crew can be designed with a loop: one agent gets historical sessions via `list_congress_sessions`, and then another iterates, calling `list_congress_amendments` for each date range.

This server touches public legislative records (treaties, committee lists, etc.). The data type is structured Congressional metadata, which the agent framework handles by defining clear, scoped operations.

You assign two specialized agents: a 'Member Agent' for `list_congress_members` and a 'Nomination Agent' for `list_congress_nominations`. They collaborate to build a comprehensive report.

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