CourtListener MCP Server

A comprehensive Model Context Protocol (MCP) server for accessing CourtListener's legal database. Provides Claude Desktop with powerful legal research capabilities including court opinions, case dockets, judge profiles, and comprehensive legal analysis.

Features

• 🏛️ Court Opinion Analysis - Retrieve and analyze court decisions with full text interpretation

• 📋 Case Docket Information - Complete case timelines, parties, and procedural history

• ⚖️ Legal Content Analysis - Extract holdings, dispositions, and factual backgrounds

• 👨‍⚖️ Judge & People Profiles - Comprehensive biographical, educational, and career data

• 🗳️ Political & Career Analysis - Political affiliations, ABA ratings, and retention events

• 💼 Position & Appointment Tracking - Judicial positions, appointment processes, and career timelines

• 🔍 Advanced Search & Citations - Search across all content types with citation network analysis

• 📊 Human-Readable Codes - Automatic translation of legal codes to plain English

• 🔗 Citation & Precedent Analysis - Track case citations and legal precedents

Related MCP server: courtlistener-mcp

Complete Tool Set

Court Opinions & Case Law

• get_opinion - Individual court decisions with full text analysis

• get_cluster - Grouped opinions (cases with multiple decisions)

• get_docket - Case information, timelines, and procedural history

• search_legal_cases - Advanced search across all legal content

• advanced_legal_search - Complex filtering and multi-court searches

Judge & People Information

• get_judge - Comprehensive judge biographical and career data

• get_positions - Judicial and professional position history

• get_political_affiliations - Political party history and timeline

• get_aba_ratings - American Bar Association ratings analysis

• get_educations - Educational background and degree details

• get_retention_events - Judicial retention votes and reappointments

• get_sources - Data provenance and source tracking

Citation & Legal Analysis

• verify_citations - Citation verification and lookup using Eyecite

• bulk_citation_verification - Batch citation processing

• find_authorities_cited - Legal precedents an opinion relies upon

• find_citing_opinions - Later cases that cite a specific decision

• analyze_citation_network - Comprehensive precedent network analysis

Courts & Infrastructure

• get_court - Court information, jurisdiction, and hierarchy

Project Structure

courtlistener-mcp/
├── courtlistener_server.py          # Main MCP server
├── server_factory.py                 # server factory
├── requirements.txt                  # Dependencies
├── .env.template                     # Environment template
├── README.md                        # Project documentation
├── core/
│   ├── __init__.py
│   └── lifespan.py                  # Lifecycle management
├── tools/                           # All 16 tool implementations
│   ├── __init__.py
│   ├── opinion_tools.py
│   ├── cluster_tools.py
│   ├── docket_tools.py
│   ├── court_tools.py
│   ├── search_tools.py
│   ├── people_tools.py
│   ├── position_tools.py
│   ├── political_affiliation_tools.py
│   ├── aba_ratings_tools.py
│   ├── retention_events_tools.py
│   ├── sources_tools.py
│   ├── education_tools.py
│   ├── citation_tools.py
│   └── opinions_cited_tools.py
├── utils/
│   ├── __init__.py
│   ├── mappings.py                  # Code mappings
│   └── formatters.py                # Output formatting
└── tests/                           # Test directory (NEW)
    ├── __init__.py                  # Make it a Python package
    ├── courtlistener_evals.py       # Comprehensive evaluation suite
    ├── mcp_test_runner.py           # Practical test runner
    ├── test_config.sh                 # Automated test script
    ├── pytest.ini                   # Pytest configuration
    └── .env.test                     # Test environment template

Installation

1. Clone and Setup

# Install MCP framework
pip install mcp

# Install legal database clients
pip install courtlistener-api
pip install brave-search-api

2. API Configuration

CourtListener Setup:

# Register for CourtListener API access at: https://www.courtlistener.com/api/
export COURTLISTENER_API_KEY="your_api_key_here"

Brave Search Setup:

# Register for Brave Search API at: https://api.search.brave.com/
export BRAVE_SEARCH_API_KEY="your_api_key_here"

3. Start MCP Servers

# Start CourtListener MCP Server
python mcp_servers/courtlistener_server.py --port 8000

# Start Brave Search MCP Server  
python mcp_servers/brave_server.py --port 8001

4. Run Training with MCP

python train_with_mcp.py \
    --config config/mcp_training.yaml \
    --mcp-servers courtlistener,brave \
    --output models/mcp_legal_agent_v1/

Learning Patterns During Training

Tool Usage Evolution

Early Training (Episodes 1-100):

• Random tool selection

• Poor query formulation

• No strategic sequencing

Mid Training (Episodes 500-1000):

• Begins preferring CourtListener for legal precedents

• Learns basic jurisdiction targeting

• Develops simple search refinement

Advanced Training (Episodes 2000+):

• Strategic tool sequencing (CourtListener → Brave for context)

• Jurisdiction-aware tool selection

• Complex query optimization

Expert Level (Episodes 5000+):

• Sophisticated search strategies

• Tool coordination patterns

• Professional-grade research workflows

Optimal Search Strategy Patterns

Precedent Research:

1. Search CourtListener for case topic

2. Search CourtListener for jurisdiction-specific cases

3. Search Brave for recent developments

Statutory Analysis:

1. Search Brave for statute text

2. Search CourtListener for statute interpretation

3. Search Brave for recent applications

General Consultation:

1. Search Brave for topic overview

2. Search CourtListener for relevant cases

3. Search Brave for practical guidance

Monitoring and Troubleshooting

Health Checks

# Test MCP server connectivity
curl http://localhost:8000/health  # CourtListener
curl http://localhost:8001/health  # Brave

Common Issues

Connection Problems:

• Check API keys are properly set

• Verify servers are running on correct ports

• Test network connectivity

Rate Limiting:

• Monitor API usage quotas

• Adjust request frequency if needed

• Scale servers for higher throughput

Performance Issues:

• Check server response times

• Monitor memory usage

• Verify database connectivity

Scaling Considerations

High-Traffic Scenarios:

• Run multiple MCP server instances

• Use load balancers for distribution

• Implement connection pooling

• Cache frequent queries

Key Benefits of Our MCP Approach

1. Strategic Learning During Training

• Agents develop optimal tool sequencing patterns naturally

• 9-call constraint forces efficient resource utilization

• Legal-specific search strategies emerge through reinforcement learning

2. Professional Legal Research Replication

• Dual database approach mirrors real legal practice (specialized + general search)

• Jurisdiction-aware tool selection develops automatically

• Citation network navigation skills learned during training

3. Research Innovation

• First implementation of MCP integration during RL training episodes

• Reproducible methodology using standardized MCP protocols

• Novel training approach for legal AI development

4. Practical Advantages

• Cost-effective training through search constraints

• Quality-focused rewards for search relevance

• Scalable architecture supporting concurrent training

Future MCP Enhancements

Additional Legal Database Integration

• Westlaw and Lexis MCP servers

• Patent database access

• Regulatory database integration

Advanced Search Capabilities

• Multi-modal document analysis

• Real-time legal news integration

• Specialized practice area databases

Performance Optimizations

• Intelligent query caching

• Predictive search suggestions

• Dynamic rate limit management

This MCP integration enables our legal AI agents to develop professional-grade research skills by learning with tools during training, representing a fundamental advancement in legal AI training methodology.