How to Use the BallotReady MCP in LlamaIndex

Q: Can I filter BallotReady tools in LlamaIndex?

You can use the allowedtools parameter to restrict the agent. This ensures your index only contains the relevant civic information.

Index live election data in LlamaIndex using the BallotReady MCP Server for high-accuracy, grounded RAG applications.

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Connect BallotReady MCP to LlamaIndex

Create your Vinkius account to connect BallotReady to LlamaIndex 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 LlamaIndex

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Index BallotReady data with LlamaIndex

Convert output from `get_elections_by_address` into a searchable vector index. LlamaIndex stores this information so your agent can query past results without re-fetching. This approach grounds your answers in real API data. You avoid the guesswork typical of standard models by forcing the agent to reference the indexed records.

Search officeholders using LlamaIndex

Run `get_officeholders` to pull current data into your knowledge base. LlamaIndex then allows for semantic search over the returned candidate profiles. Your application queries the index rather than the raw API. This makes your RAG pipeline faster and more reliable when handling complex civic questions.

Sync BallotReady records in LlamaIndex

Use `list_positions` to find open seats and index the descriptions. LlamaIndex organizes these into a format that the FunctionAgent can traverse efficiently. This creates a persistent memory for your AI. It knows exactly which offices are up for election because the data is saved in your local vector store.

Setup guide

Set up BallotReady MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all BallotReady MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to BallotReady tools.",
)
response = await agent.run("List recent BallotReady data")

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Common questions about BallotReady MCP in LlamaIndex

You use the McpToolSpec to retrieve the tools and then pass the outputs into your ingestion pipeline. LlamaIndex handles the document conversion automatically.

Absolutely. Once the MCP tools return the candidate or election data, you map those results to your vector store for contextual querying.

You can use the allowed_tools parameter to restrict the agent. This ensures your index only contains the relevant civic information.

You simply re-run your indexing agent. LlamaIndex updates the knowledge base with the latest API responses from the tools.

Your address is used as a transient parameter to fetch election data. We never store your location coordinates in the vector index, ensuring that only the public civic records persist.

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