ai-orders-agent

*The live deployment at ai-orders-agent.vercel.app.*

Agent-facing service for the AI Court Orders dataset — one Vercel project that exposes the same queries four ways:

• Chat (/chat) — a hosted chat UI; ask in plain English, no setup. A funded, rate-limited LLM (OpenRouter DeepSeek by default) answers by calling the dataset tools (see Chat below).

• MCP (/api/mcp) — add as a custom connector in Claude Desktop / claude.ai, or any MCP client.

• OpenAPI (/openapi.json) — import into a ChatGPT GPT (Actions) or any function-calling LLM.

• REST/JSON (/api/*) — call directly from code or the browser.

It is read-only and stateless: it live-fetches the dataset the AI-orders-explorer repo publishes (that repo owns the data pipeline, the human web explorer, and the Claude-Code skill — this one is purely the agent surface) and caches it in memory.

Data source

Set ORDERS_DATA_BASE to where the dataset (explorer_data.json and bar_opinions.json) is published. Defaults to the live published copy at https://legalhack.io/data (943 records). See .env.example.

Related MCP server: German Law MCP Server

Endpoints

Filters (search/list/facets): judge (title-insensitive), court (alias-aware: sdny/S.D.N.Y./full name), state, type, consequence, ai_type, applies_to (multi-value), source, jurisdiction, tag, requires, date_from, date_to, has_pdf, has_link.

requires=<key> matches records whose reqs[key] is set — disclose (~128), certify_if_ai (~106), verify, prohibited, certify_all, proprietary — answering "which courts require AI disclosure / a certification?". Because facets honors every filter, facets?field=court&consequence=sanctions_attorney ranks courts by attorney-sanction count and facets?field=court&requires=disclose ranks them by disclosure requirements. The compact projection includes summary.

Chat

/chat is a streaming chat UI. The backend (POST /api/chat) runs a bounded tool-calling loop: an LLM answers questions by calling the same dataset operations the MCP/REST surfaces use (tool definitions are shared via lib/tools.ts, so the surfaces never drift). It is read-only — the tools only search and read the public dataset.

Provider. Default is OpenRouter (CHAT_PROVIDER=openrouter) on a cheap DeepSeek model. Set CHAT_PROVIDER=anthropic or openai to switch; each reads its own server-only key. Keys never reach the browser.

Rate limiting. The funded path is protected by three independent limits — per-IP burst, per-IP daily cap, and a global daily kill-switch — backed by Upstash Redis. Set UPSTASH_REDIS_REST_URL and UPSTASH_REDIS_REST_TOKEN for the public deploy. Without Upstash, limiting falls back to a best-effort in-memory limiter that is not durable across Vercel invocations — fine for local dev, not safe for a public funded endpoint.

See .env.example for all chat/rate-limit variables.

Run / deploy

npm install
npm run dev        # http://localhost:3000  (try /chat, /api/stats, /openapi.json, /api/mcp)
npm test           # query-logic parity tests
vercel deploy      # or push to a Vercel-connected GitHub repo

Connect it

• Claude (Desktop or claude.ai): add a custom connector pointing at https://<your-deploy>/api/mcp.

• ChatGPT: create a GPT → Actions → import https://<your-deploy>/openapi.json.

• Other LLMs / code: call the REST endpoints, or use the OpenAPI spec for function-calling.

Query behavior (court aliasing, title-insensitive judge match, multi-value applies_to, facet placeholder handling) mirrors the explorer's orders CLI.

License

MIT — see LICENSE.