How to Use the BallotReady MCP in CrewAI

Deploy autonomous research teams to track elections using the BallotReady MCP Server and CrewAI.

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Connect BallotReady MCP to CrewAI

Create your Vinkius account to connect BallotReady 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 BallotReady with CrewAI

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Coordinate Election Research

This CrewAI integration lets you assign specific `list_elections` tasks to dedicated AI personas. A researcher agent gathers the raw facts while an analyst agent interprets the political impact. You give the researcher access to the MCP Server to find upcoming dates. Once it finds a match, it writes the details to shared memory so the analyst can immediately start pulling historical context.

Map Local Power Structures

A mapping agent uses `get_districts_by_address` to establish the geographical boundaries of a neighborhood. Understanding who controls a specific region requires crossing multiple data points. That agent hands the district IDs to a profiling agent equipped with `get_officeholders`. The crew autonomously builds a complete organizational chart of every local politician representing that exact block.

Build Candidate Dossiers

You set up a monitor agent that continuously runs `list_candidates` to watch for new filings. Tracking challengers across hundreds of local races is impossible for a human team. When a new name appears, a separate background investigator agent takes over. It executes `get_candidate` to pull their biographical data, formats a summary report, and escalates it to a human moderator.

Setup guide

Set up BallotReady 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 BallotReady tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent BallotReady 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="BallotReady Analyst",
        goal="Access and analyze BallotReady data via MCP.",
        backstory="Expert analyst with direct BallotReady access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent BallotReady 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 BallotReady. 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 BallotReady MCP in CrewAI

Install the framework with the tools extra enabled via pip. Pass your endpoint URL directly into the server array parameter on your Agent definition.

Yes, you limit access using the tool filter parameter. This prevents your researcher agent from calling `get_account_check` while allowing it to use `get_ballot_measures`.

They rely on the framework's shared memory system. The first agent runs `list_positions` and stores the output, allowing downstream agents to read the results without making duplicate API calls.

The framework supports stdio, SSE, and Streamable HTTP out of the box. HTTP is generally the easiest to configure for external API endpoints like this one.

All biographical queries run inside an isolated, ephemeral sandbox. Vinkius destroys the container memory the exact second `get_candidate` returns the profile, leaving no trace of your research targets.

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