Docket Intelligence

Monitors court dockets and cross-references them against a client database to surface business development opportunities and conflict flags for law firms.

Built with MCP (Model Context Protocol), Claude, and Python. Runs locally with SQLite; deploys to Azure with SharePoint as the data layer.

Architecture

Scheduler (cron / Azure Logic App)
        │
        ▼
Agent Orchestrator  ←──── Claude API (entity extraction + classification)
        │
        ├──► Docket Monitor MCP Server   (CourtListener / Docket Alarm)
        ├──► Client Intel MCP Server     (SQLite locally / SharePoint in prod)
        └──► Notifications MCP Server    (log file locally / Graph API in prod)

Related MCP server: DocketBird MCP Server

Local Setup

1. Clone and install dependencies

git clone <repo>
cd docket-intelligence
python -m venv .venv
source .venv/bin/activate       # Windows: .venv\Scripts\activate
pip install -r requirements.txt

2. Configure environment

cp .env.example .env
# Edit .env and add your ANTHROPIC_API_KEY at minimum

3. Seed the local client database

python scripts/seed_clients.py

4. Run the agent

# Dry run — fetches and analyzes dockets but writes nothing
python -m src.agent.orchestrator --dry-run

# Live run — logs opportunities to SQLite, sends mock notifications
python -m src.agent.orchestrator

# Filter by court and date
python -m src.agent.orchestrator --court nysd --date-from 2024-01-01

5. Run tests

pytest tests/ -v

Project Structure

docket-intelligence/
├── src/
│   ├── models/
│   │   └── models.py              # Pydantic data models (Docket, Client, Opportunity, …)
│   ├── mcp_servers/
│   │   ├── docket_monitor/
│   │   │   └── server.py          # MCP server: fetches dockets from CourtListener
│   │   ├── client_intel/
│   │   │   ├── server.py          # MCP server: client DB operations
│   │   │   └── sqlite_repo.py     # SQLite adapter (swap for Graph adapter in prod)
│   │   └── notifications/
│   │       └── server.py          # MCP server: Teams/email/tasks (logs locally)
│   └── agent/
│       └── orchestrator.py        # Core AI loop connecting all three servers
├── scripts/
│   └── seed_clients.py            # Populate local DB with test clients
├── tests/
│   └── test_client_repo.py        # Unit tests for SQLite repo and matching
├── data/                          # Local SQLite DB and notification logs (git-ignored)
├── .env.example
└── requirements.txt

Swapping to Production (Microsoft)

The local → production swap is controlled by one env variable: ENV=production.

When ENV=production, the Client Intel server loads graph_adapter.py instead of sqlite_repo.py. The MCP tool interface is identical — only the data layer changes.

See DEPLOYMENT.md for Azure setup instructions.

CourtListener Wrapper Server

src/mcp_servers/courtlistener_wrapper/server.py is a unified server that combines the official CourtListener hosted MCP server with this project's conflict-checking and opportunity-management tools. Use it when you want a single connection point instead of running three separate servers.

What it exposes

Connecting

If COURTLISTENER_API_TOKEN is set, the wrapper connects to the official CourtListener MCP server via OAuth SSE and proxies its full tool set. Without a token it runs in local-only mode (direct REST API + conflict tools only).

# Run the wrapper standalone (e.g. to wire into Claude Desktop or another MCP host)
python -m src.mcp_servers.courtlistener_wrapper.server

To point the orchestrator at the wrapper instead of the three individual servers, replace the StdioServerParameters in orchestrator.py with a single entry:

WRAPPER_SERVER = StdioServerParameters(
    command="python",
    args=["-m", "src.mcp_servers.courtlistener_wrapper.server"],
)

Adding a New Docket Data Source

1. Create src/mcp_servers/docket_monitor/adapters/your_source.py

2. Implement fetch_dockets(...) returning list[Docket]

3. Set DOCKET_SOURCE=your_source in .env

4. The server picks up the new adapter via the factory in server.py

Key Design Decisions

• Adapter pattern — every external dependency sits behind an interface, making the local↔production swap clean and testable without cloud access.

• MCP over direct function calls — each server can be tested, replaced, or scaled independently. The agent only knows tool names and schemas, not implementations.

• Human in the loop — the agent surfaces and classifies; attorneys decide. No automated outreach without human approval.

• Tenant-local in production — client data never leaves the Microsoft 365 tenant. The only external calls are reads from court data APIs and the Claude API.