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recht-mcp

by nulegal-startup
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Python
Hybrid

Search German law

search
Read-onlyIdempotent

Query both German federal and state statutes and court decisions with a single query, getting parallel provisions across jurisdictions and case results anchored to paragraphs.

Instructions

One query over BOTH corpora: federal and Land statutes (lexical, with concept pinning) and court decisions (semantic — natural-language questions work well here and are the better shape for case law).

Search both unless you have a reason not to. A term of art often does not appear in the statute that governs it: 'Verzugspauschale' matches no provision (§ 288 BGB says 'Pauschale in Höhe von 40 Euro') while 184 decisions use the word. scope='norms' alone will read as 'nothing here' in exactly those cases.

CROSS-LAND COMPARISON: a single query returns the parallel provisions of the Bund and of every covered Land side by side, each row jurisdiction-labelled, plus a by_jurisdiction roll-up. Ask 'Videoüberwachung öffentlich zugänglicher Räume' and you get BDSG § 4 next to the Land data-protection and police provisions. Full text is held for Bayern, Brandenburg, Nordrhein-Westfalen and Sachsen.

Decision hits come back already anchored at the best-matching Randnummer (…#rd_51), so you can quote a paragraph rather than a document, AND carry doknr — the key listCasePassages, listCitedAuthorities and listCitingDecisions take, so you can go straight from a search hit to that decision's passages or its authorities without resolving anything first. Query in German; write raw umlauts, they are handled.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qYesGerman query. Keywords, a citation, or a full question.
limitNo
scopeNo'all' (default) searches both. Narrow only when you know which corpus answers.all
include_repealedNoInclude repealed (aufgehobene) provisions. Off by default; turn it on when researching an older state of the law.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.1

TDQS

A4.8/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the read-only/idempotent annotations, the description discloses important behavior: lexical search with concept pinning for statutes, semantic search for decisions, cross-Land jurisdiction roll-ups, full-text coverage for four Länder, best-match Randnummer anchoring, doknr propagation, and umlaut handling. This is substantial contextual disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but dense, with front-loaded essentials and clear section-like paragraphs. Every sentence contributes useful information, and the examples illustrate rather than pad.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex, two-corpus tool with no output schema, the description covers the important invocation, scope, result-shape, and follow-up workflow. It stops short of describing pagination, result ordering, or exact response structure, but the schema covers limit constraints and the rest is sufficient for correct use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 75%, and the description significantly expands meaning for `q` (German, natural-language questions, raw umlauts) and `scope` (why narrowing to norms can fail, default 'all' behavior). It does not add anything about `limit`, but the parameter is relatively self-explanatory.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description identifies a specific action and resource: 'One query over BOTH corpora: federal and Land statutes ... and court decisions'. It distinguishes this search tool from sibling retrieval/list tools by emphasizing the dual-corpus search behavior and result features like `doknr` and `#rd_51` anchors.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit when-to-use and when-not-to-use guidance: 'Search both unless you have a reason not to', warns that scope='norms' alone reads as empty for terms of art, recommends natural-language questions for case law, and tells the agent how to proceed with sibling tools using `doknr`.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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