Tw Legal Rag MCP Server

Taiwan Legal RAG (twlegalrag)

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Open-source CLI for semantic Taiwan legal judgment retrieval, powered by Legal Detective's 22M-judgment retrieval infrastructure.

Taiwan Legal RAG CLI retrieves Taiwan court judgments from Legal Detective's public TLR endpoint and packages them for use with your own AI tools. It does not generate legal advice, does not call any LLM, and does not guarantee semantic faithfulness of third-party model outputs. Its built-in citation check only verifies whether cited judgments belong to the retrieved bundle.

Why it is different

This is not a generic keyword judgment search tool. It connects to the TLR retrieval service that Legal Detective has been building for a long time:

• About 22 million Taiwan court decisions, structurally processed and vectorized.
• Thousands of hours of retrieval pipeline optimization.
• Semantic fuzzy search — not limited to docket numbers, court names, or keywords; you can use natural language to find judgments that are "conceptually similar but worded differently."
• Exact docket lookup (v1.1) — when the query is a complete Taiwan docket number (e.g. 最高法院112年度台上字第9號), the tool automatically switches to exact lookup and returns that case's own documents (civil/criminal cases sharing the same number are listed side by side with labels). When nothing is found it says so explicitly: "not found does not mean the judgment does not exist; do not speculate about the case" — it never pads the result with semantically similar cases.
• Appeal chain case_history (v1.1) — when reading a judgment's full text, the database-recorded upper/lower instances are attached (including a flag for 主文含「廢棄」, i.e. the holding was vacated on appeal). You can see whether a judgment has been vacated by a higher court before citing it. Absence of an upper-court record only means the database has no record; it does not mean the judgment is final.
• Exact administrative-interpretation lookup get_legal_reference (2026-08, hosted MCP) — look up an administrative interpretation (函釋) by its issuing serial number (e.g. 台財稅第881945861號) and get its full text plus a lifecycle status (verified-active / unverified / repealed / no-longer-applied / superseded). Verify existence and validity before citing an interpretation; a miss explicitly states that not found does not mean the interpretation does not exist. Interpretations and judgments are strictly separated: never mixed in one ranking, and never to be cited as court reasoning. See docs/mcp-anchor.md.
• Citation protection is a first-class citizen, not an afterthought — each bundle carries an allowed_citations whitelist (only judgments whose reasoning text was actually read in), unread_candidates markers (judgments whose reasoning was not read must not be cited as authority), verification instructions written into every bundle (including opinion-layer self-check), plus a bundle-level citation check on the CLI side. The whole design targets the most painful hallucination pattern in legal AI: real case number, fabricated holding. Ordinary retrieval tools stop at handing data to the model; here, citation discipline is part of the data format itself.
• The open-source CLI does not embed the judgment corpus and does not expose backend model weights or vector indexes; it is a client for the public TLR retrieval endpoint.

Compared with "official-website wrapper" tools

Another common approach is to proxy the Judicial Yuan / law database websites' built-in search in real time. The two serve different purposes and can complement each other:

The wrapper's strength is real-time official-source access; this tool's strength is semantic retrieval quality and citation discipline.

Unlike keyword-only legal search tools, Taiwan Legal RAG CLI connects to a production semantic retrieval backend built on 22M+ Taiwan court judgments, enabling fuzzy concept-level search while keeping model weights, infrastructure, and private indexes server-side.

(Wording note: what is open-sourced is the CLI, not the model or the vector store; the backend retrieval service, model weights, and private indexes stay server-side and are not published with this tool.)

What it does / does not do

Does: retrieve judgments with natural language → get a structured listing, judgment reasoning excerpts, and citation links → package them into a bundle for your own AI; and run a bundle-level citation check on any AI-generated answer.

Does not: this tool calls no LLM, generates no legal opinion, and endorses no model output. Answers are produced by the AI you choose (ChatGPT / Claude / Gemini / a local model).

What the built-in citation check can verify

check is a bundle-level, best-effort string check. It only verifies:

• whether the case numbers cited in the answer are inside the bundle (catching "cited a number not in the bundle" = suspected fabrication);
• citations of judgments outside the bundle, or nonexistent ones;
• quote existence (bundle level): whether a verbatim sentence the answer attributes to "the court said…" appears anywhere in the bundle text.

What it cannot verify (important)

• whether a quote comes from the specific judgment the answer attributes it to (existence check only looks at "does this sentence appear anywhere in the bundle", not bound to a particular judgment);
• whether the court's holding was read correctly;
• whether a party's argument (plaintiff/defendant/appellant) was mistaken for the court's holding;
• whether obiter dicta was treated as the judgment's core authority;
• paraphrase-style holding hallucinations.

All of these require reading the full judgment text — which is why bundles include judgment excerpts and verification instructions that require the downstream model to verify on its own. pass only means "the cited numbers match the bundle's identity list"; it does not mean "the legal reasoning is correct" or "the quote really comes from that judgment." Also, check only compares against bundle content, not the entire Legal Detective database — if you later open full judgment texts yourself and rewrite the answer, check still only sees the excerpts originally packed.

Install

pip install twlegalrag

Depends only on httpx / typer / rich. No LLM packages or keys needed — this tool does not call LLMs.

Usage

# 1) Pure retrieval — list matching judgments
twlegalrag search "勞資 加班費" -n 5 --read

# 2) Pack — produce a bundle you can hand to any AI  ★ main flow
twlegalrag pack "車禍對方全責,我可以求償什麼?" -o bundle.json
#   → paste bundle.json to ChatGPT / Claude / Gemini and require it to cite
#     only judgments inside the bundle

# 3) Citation check — bundle-level check on any AI-generated answer
twlegalrag check bundle.json answer.txt

# Service health
twlegalrag health

A pack bundle contains query, each judgment's citation_id (J1, J2, ...), citation_text, citation_url, doc_id, the Layer-1 listing, fulltext_excerpt (an excerpt of the judgment's reasoning, length-capped), case_history (database-recorded appeal chain, v1.1), allowed_citations, and a verification_instructions block that explicitly requires the downstream model to cite only in-bundle judgments and to mark unsupported propositions as unverified. An AI USE NOTICE is also printed to stderr.

Since v1.1, verification_instructions additionally includes an OPINION-LAYER SELF-CHECK: after answering, the downstream model must go back and verify that (a) every holding attributed to a judgment actually appears in that judgment's excerpt (not another judgment's, not inferred); (b) outcome directions (win/lose/vacated/dismissed/remanded) are not reversed; (c) judgments shown as vacated in case_history are not cited as currently valid holdings. This complements check's bundle-level number check — a real case number does not make the attributed holding real, and opinion-layer verification can only be done by the model that read the text; these rules write that obligation into every bundle.

allowed_citations is the whitelist of citable judgments and only contains judgments whose reasoning text was actually read in. The CLI's pack reads every judgment it returns, so the two always match. For the hosted Remote MCP search_bundle (/v1/pack), when read_top < max_results, only the top read_top judgments are read in full; the rest remain listed in judgments for browsing but are moved to unread_candidates (not authority; must not be cited as court reasoning). See docs/mcp-anchor.md.

Configuration (optional)

By default the CLI talks to the public endpoint https://tlr.dr-lawbot.com, no key required. If the service operator issues you an API key, put it in an environment variable or ~/.twlegalrag/config.toml (git-ignored — never commit it):

export TWLEGALRAG_TLR_BASE_URL=https://tlr.dr-lawbot.com   # default
export TWLEGALRAG_TLR_API_KEY=...                          # optional

[tlr]
# base_url = "https://tlr.dr-lawbot.com"
# api_key  = "..."

Privacy and data flow

First, what never passes through the TLR server:

• Your full conversation with your AI (Claude / ChatGPT / local model), your uploaded documents, and the AI-generated answers all happen between you and your AI provider and never pass through TLR. TLR is a retrieval-only server; the only things it receives are the retrieval query strings your AI client decides to send and the subsequent judgment-document requests.
• No account registration: the public REST endpoint works without a key, the service has no user account system, and queries are not tied to any account identity.
• The judgment data itself consists of Taiwan's publicly available court decisions; responses contain no non-public personal data.

What does travel over the network, and you should understand:

• Your search terms / questions are sent to the TLR retrieval endpoint (https://tlr.dr-lawbot.com) to fetch judgments.
• TLR may log your query text, timestamp, IP-derived metadata, and result counts for retrieval-quality analysis. Do not submit personal secrets or confidential facts. Queries are not used to train generative models.
• This tool calls no LLM and uses no server-side tokens; if you feed a bundle to some AI yourself, that transmission and its cost happen between you and your chosen AI provider and have nothing to do with this tool.
• If you have an endpoint API key, keep it in environment variables; do not commit config files.

How the citation check works

twlegalrag/faithful/ is a set of zero-dependency pure functions (standard library re + unicodedata only). Given the answer text and the bundle's judgment excerpts, it returns pass / needs_review / fail. It is deliberately conservative: when unsure it returns needs_review rather than fail to keep false alarms low. It calls no LLM and touches no database; it is deterministic string analysis.

⚠️ This directory is a snapshot of internal code; some functions in it (e.g. check_party_as_court / run_all_checks) are not used by the CLI. Their presence does not mean the CLI can do opinion-layer / semantic verification — the CLI uses only two bundle-level checks. Do not read the file list as a feature list. See twlegalrag/faithful/VENDORED.md.

Other ways to connect (same TLR backend)

This CLI is one way to use the TLR retrieval service. The same backend tlr.dr-lawbot.com also supports plugging judgment search directly into your AI tools via Remote MCP. Both use the same MCP endpoint https://tlr.dr-lawbot.com/mcp; OAuth completes automatically on connection (dynamic registration, no API key application or setup needed):

• Claude (Remote MCP): Settings → Connectors → Add custom connector, URL https://tlr.dr-lawbot.com/mcp.
• ChatGPT (MCP connector): add a custom MCP server in Connectors, URL https://tlr.dr-lawbot.com/mcp.
• Claude Code (Skill, via CLI not MCP): this repo ships a ready-made skill in skills/tw-legal-rag/ — drop the whole folder into your project's .claude/skills/. It wraps this CLI's pack subcommand so Claude automatically retrieves judgments when you ask about Taiwan case law and is required to cite only in-bundle citation_ids. The included scripts/search_judgments.py auto-locates the executable and handles two Windows pitfalls (see the skill's SKILL.md).

The Remote MCP surface currently has four tools: search_bundle, search_judgments, get_judgment_fulltext, and get_legal_reference added in 2026-08 (exact administrative-interpretation lookup, see docs/mcp-anchor.md); the last one is not wired into this CLI yet.

This server is listed in the official MCP Server Registry as io.github.aa0101181514/tw-legal-rag.

Whether you go through the CLI, MCP, or the Claude Code skill, answers are generated by your own AI; this service only provides judgment content and verifiable citation links.

Architecture

your question
   │
[retrieve]  TLR /v1/search   ──►  Layer-1 listings + result_token
   │        TLR /v1/fulltext ──►  reasoning excerpt per judgment (capped)
   │
[pack]      pack ──► bundle.json (citation_id / allowed_citations / verification rules)
   │                 └─► hand to your own AI tool
   │
[check]     check ──► bundle-level citation check (in/out of bundle + in-bundle quote existence)

The judgment corpus, embeddings, and retrieval logic live server-side and are not in this repo. This CLI is the open-source client and citation-check tool.

Disclaimer

This tool is an analysis aid, not legal advice, and not a lawyer. Always read the full text of cited judgments yourself. Judgments obtained through the API are Taiwan's publicly available court decisions; you are responsible for your own use.

License

MIT.

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mcp-name: io.github.aa0101181514/tw-legal-rag