CourtListener MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add CourtListener as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to CourtListener. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in CourtListener?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About CourtListener MCP Server
Empower your AI agent to orchestrate your entire legal research workflow with CourtListener, the world's leading free and open platform for legal data. By connecting CourtListener to your agent, you transform complex legal searches into a natural conversation. Your agent can instantly search for opinions, audit court dockets, and retrieve detailed judge information without you ever touching a technical portal. Whether you are conducting case law research or monitoring judicial activity, your agent acts as a real-time legal assistant, ensuring your research is always grounded in open and accessible data.
LlamaIndex agents combine CourtListener tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Opinion Auditing — Search for legal opinions by keyword and retrieve detailed metadata, including court name and date filed.
- Docket Oversight — Query court dockets to stay on top of ongoing litigation and case filings in real-time.
- Judicial Intelligence — Retrieve detailed information about judges and their financial disclosures to maintain strict control over your research context.
- Citation Discovery — List citations for specific opinions to understand the legal network and precedents.
- Court Governance — List all available courts and their metadata to ensure your jurisdictional research is accurate.
The CourtListener MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect CourtListener to LlamaIndex via MCP
Follow these steps to integrate the CourtListener MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from CourtListener
Why Use LlamaIndex with the CourtListener MCP Server
LlamaIndex provides unique advantages when paired with CourtListener through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine CourtListener tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain CourtListener tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query CourtListener, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what CourtListener tools were called, what data was returned, and how it influenced the final answer
CourtListener + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the CourtListener MCP Server delivers measurable value.
Hybrid search: combine CourtListener real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query CourtListener to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying CourtListener for fresh data
Analytical workflows: chain CourtListener queries with LlamaIndex's data connectors to build multi-source analytical reports
CourtListener MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect CourtListener to LlamaIndex via MCP:
get_court
Get details for a specific court
get_judge
Get details for a specific judge
get_opinion
Get details for a specific opinion
list_citations
List citations for an opinion
list_courts
List all courts
list_financial_disclosures
List judge financial disclosures
list_judges
List judges
list_opinions
List opinions
search_dockets
Search for court dockets
search_opinions
Search for legal opinions
Example Prompts for CourtListener in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with CourtListener immediately.
"Search for court opinions about 'copyright fair use'."
"Show me details for judge ID 1234."
"Check for dockets related to 'SpaceX' in 2024."
Troubleshooting CourtListener MCP Server with LlamaIndex
Common issues when connecting CourtListener to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpCourtListener + LlamaIndex FAQ
Common questions about integrating CourtListener MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect CourtListener with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect CourtListener to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.