How to Use the CourtListener MCP in Mastra AI

Q: How do conditional branches work with CourtListener data?

You route logic based on the payload. If searchopinions finds a Supreme Court ruling, Mastra routes it to a specialized summarizer agent. Lower court rulings go to a different processing queue.

Build resilient legal research workflows in Mastra AI that automatically retry failed docket searches.

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Connect CourtListener MCP to Mastra AI

Create your Vinkius account to connect CourtListener to Mastra AI 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 CourtListener with Mastra AI

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Perplexity

Automate Precedent Workflows

The `search_opinions` tool kicks off your Mastra AI workflow based on specific keywords. Legal research involves deep rabbit holes. If the initial query returns zero hits, the engine automatically branches to broaden the search terms. Once it finds relevant cases, the agent pulls the full text using `get_opinion`. The built-in exponential backoff means you never crash if the upstream API hits a rate limit during a heavy data pull.

Trace Rulings via MCP

Calling `list_citations` builds a complete precedent tree from a starting case. Finding related rulings requires multiple API hops. Mastra handles the state management across these sequential tool calls. You add a human-in-the-loop step right before the agent summarizes the findings. Setting `requireToolApproval` on the tool pauses the workflow until an attorney reviews the pulled cases.

Track Judge Disclosures via MCP Server

The `list_financial_disclosures` tool builds reliable data pipelines for investigative journalists tracking judicial conflicts. You set up this MCP Server to monitor specific jurisdictions periodically. If it flags a potential conflict, the workflow hits `get_judge` to pull their full biography. Mastra then routes the complete dossier to your Slack channel, retrying automatically if the webhook drops.

Setup guide

Set up CourtListener MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All CourtListener tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "courtlistener-mcp-client",
  servers: {
    "courtlistener-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "CourtListener Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to CourtListener tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent CourtListener transactions"
);
console.log(result.text);

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by CourtListener. 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 CourtListener MCP in Mastra AI

Run npm install @mastra/mcp@latest. Instantiate MCPClient with your Vinkius URL, call mcpClient.listTools(), and spread the array directly into your agent's tool configuration.

Yes. Mastra automatically applies exponential backoff if a search_dockets call times out. The workflow engine manages the retries without you writing custom error handling.

You can pause any workflow before execution. Set requireToolApproval to true on your tool setup, and Mastra waits for your signal before executing get_opinion or pulling sensitive files.

You route logic based on the payload. If search_opinions finds a Supreme Court ruling, Mastra routes it to a specialized summarizer agent. Lower court rulings go to a different processing queue.

Vinkius isolates every session in a zero-trust V8 sandbox. Your queries for specific judges and financial disclosures execute and immediately die. We store zero persistent data.

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