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metaneutrons

German Legal MCP Server

by metaneutrons

dip_search

Find German parliamentary documents (Bundestagsdrucksachen) by exact title substring. Use one distinctive word to get matches; avoid multi-word phrases unless copied verbatim.

Instructions

Search Bundestagsdrucksachen (parliamentary documents) via DIP API. Matches title as a literal substring — NOT a keyword AND/OR search. An extra word breaks the match unless it appears verbatim, in that exact order, in the title. For example "Tiergesundheitsgesetz" alone finds dozens of documents, but "Tiergesundheitsgesetz Änderung" finds none, because real titles read "...Änderung des Tiergesundheitsgesetzes..." — reversed order and a different word ending. Prefer a single distinctive word (e.g. a law's short title) over a natural-language phrase. Returns metadata: Dokumentnummer, title, type, date, PDF URL. Use dip_get to retrieve full text (e.g., Gesetzesbegründung) of a specific Drucksache.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoDrucksachetyp filter: "Gesetzentwurf", "Beschlussempfehlung und Bericht", "Kleine Anfrage", "Große Anfrage", "Antrag", etc.
limitYesMax results (default: 10)
queryYesA single distinctive word, or an exact phrase copied verbatim from the title — this is a literal substring match, not AND/OR keyword matching. Adding descriptive words not verbatim in the title (even correct ones, in a different order) returns zero results.
date_endNoEnd date (YYYY-MM-DD)
date_startNoStart date (YYYY-MM-DD)
herausgeberNoBT = Bundestag, BR = Bundesrat
wahlperiodeNoLegislative period (e.g., 20, 21)
Install Server

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations, the description carries the full behavioral burden, and it delivers: it discloses the literal-substring matching rule, the 'NOT keyword AND/OR' semantics, a concrete failure example, and the reverse-order/word-ending pitfall. It also states the return metadata fields. This far exceeds typical behavioral 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?

Every sentence earns its place: purpose, core matching constraint, illustrative example, search recommendation, return payload, and pointer to dip_get. The longer-than-usual length is justified by the unusual search semantics that must be communicated to avoid failed queries.

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

Completeness5/5

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

For a search tool with no output schema, the description is complete: it explains what is searched, how the search behaves, what results look like, and how to get full documents. Filter parameters like type, date, and wahlperiode are adequately covered by the schema.

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 100%, so the baseline is 3. The description adds strong value for the query parameter by explaining the required verbatim substring behavior and recommending single distinctive words, going well beyond the schema's generic wording. Other parameters are already fully documented in the schema.

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 states a specific verb and resource: 'Search Bundestagsdrucksachen (parliamentary documents) via DIP API.' It clearly distinguishes from siblings by document type (Drucksachen vs Vorgang/Plenarprotokoll) and from dip_get by noting the difference between search and full-text retrieval.

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?

It gives explicit usage guidance: match titles as literal substrings, prefer a single distinctive word, avoid natural-language phrases, and use dip_get instead when full text is needed. This tells the agent both how to use this tool and when to delegate to an alternative.

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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