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by com.planitprima.lawbster
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Look Up Multiple Provisions

legal_lookup_batch
Read-onlyIdempotent

Retrieve multiple cited provisions or decisions in a single call without semantic fallback. Accepts 1-20 citations — norm citations and court-decision references, same forms as legal_lookup (e.g. ['§ 823 BGB', 'Art. 6 DSGVO', 'VII ZR 184/14']). Citations not found appear as found=false. Norm citations require an exact match. Decision references follow the same resolution as legal_lookup and can yield ambiguous, relaxed-reference or title-only matches; is_exact_match and the hint report that uncertainty. Each entry has its own match status and any ambiguity or truncation hint. Decision matches return the lead section, not the complete judgment. Fuzzy resolution of a hard-to-find provision belongs to legal_lookup, not to this tool.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
languageNoDocument language: 'de' (default, German-language documents from German and EU sources) or 'en' (currently EDPB/EDSA data-protection guidance only). DE and EN are separate corpora — a document published in both languages has one entry per language under the same abbreviation. 'en' selects the English corpus regardless of the answer language; 'edsa' is published mostly in English.
citationsYesList of citation strings, e.g. ['§ 823 BGB', 'Art. 6 DSGVO']. Max 20.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / language / anyOf
      Previous value: -[
-  {
-    "description": "Content language of legal documents.\n\nUses StrEnum so values compare equal to plain strings for\nseamless PostgreSQL/Qdrant compatibility.",
-    "enum": [
-      "de",
-      "en"
-    ],
-    "type": "string"
-  },
-  {
-    "type": "null"
-  }
-]New value: +[
+  {
+    "description": "Document language, independent of the language of the answer.",
+    "enum": [
+      "de",
+      "en"
+    ],
+    "type": "string"
+  },
+  {
+    "type": "null"
+  }
+]
    • changedInput schema / properties / language / description
      Previous value: -"Corpus language: 'de' (default — the full German + EU corpus) or 'en' (English-language corpus: currently EDPB/EDSA data-protection guidance only; much smaller than 'de'). DE and EN are separate corpora — a document published in both languages has one entry per language under the same abbreviation. 'en' selects the English corpus regardless of the answer language; 'edsa' is published mostly in English."New value: +"Document language: 'de' (default, German-language documents from German and EU sources) or 'en' (currently EDPB/EDSA data-protection guidance only). DE and EN are separate corpora — a document published in both languages has one entry per language under the same abbreviation. 'en' selects the English corpus regardless of the answer language; 'edsa' is published mostly in English."
  2. Changed1 schema field changed
    • changedInput schema / properties / language / description
      Previous value: -"Corpus language: 'de' (default — the full German + EU corpus) or 'en' (English-language corpus: currently EDPB/EDSA data-protection guidance only; much smaller than 'de'). DE and EN are separate corpora — a document published in both languages has one entry per language under the same abbreviation. Set 'en' only when the user explicitly works in English or asks for the English version."New value: +"Corpus language: 'de' (default — the full German + EU corpus) or 'en' (English-language corpus: currently EDPB/EDSA data-protection guidance only; much smaller than 'de'). DE and EN are separate corpora — a document published in both languages has one entry per language under the same abbreviation. 'en' selects the English corpus regardless of the answer language; 'edsa' is published mostly in English."
  3. Changed1 schema field changed
    • addedInput schema / properties / language
      Added value: +{
+  "anyOf": [
+    {
+      "description": "Content language of legal documents.\n\nUses StrEnum so values compare equal to plain strings for\nseamless PostgreSQL/Qdrant compatibility.",
+      "enum": [
+        "de",
+        "en"
+      ],
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "Corpus language: 'de' (default — the full German + EU corpus) or 'en' (English-language corpus: currently EDPB/EDSA data-protection guidance only; much smaller than 'de'). DE and EN are separate corpora — a document published in both languages has one entry per language under the same abbreviation. Set 'en' only when the user explicitly works in English or asks for the English version."
+}
  4. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, consistent with the description's 'Retrieve' framing. The description adds valuable behavior beyond annotations: no semantic fallback, not-found entries report found=false, exact-match requirement for norms, ambiguous/relaxed/title-only matches for decisions reported via is_exact_match and hints, and that decision matches return only the lead section. No contradiction with annotations.

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

Conciseness4/5

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

The description is front-loaded with the core purpose and each subsequent sentence carries distinct information: batch size, citation forms, not-found behavior, matching semantics, ambiguity reporting, and lead-section return. It is dense but slightly verbose in places (e.g., restating is_exact_match and hint reporting), though no sentence is pure filler.

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?

With an output schema present, return values are already covered structurally. The description additionally handles batch limits, matching semantics, ambiguity/truncation hints, and lead-section behavior. The language parameter is fully documented in the schema (100% coverage). Minor gaps like explicit error handling for invalid citation formats are not essential given the thorough schema.

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

Parameters3/5

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

Schema coverage is 100%, so the schema already documents both parameters well. The description adds meaningful context for the citations parameter (example forms matching legal_lookup, the found=false behavior, exact-match vs relaxed-match semantics), which slightly elevates it, but most parameter detail is already in the schema. Baseline 3 is appropriate.

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 opens with a specific verb+resource statement ('Retrieve multiple cited provisions or decisions in a single call') and immediately distinguishes the tool from its sibling by adding 'without semantic fallback' and later routing fuzzy resolution explicitly to legal_lookup. An agent can tell this batch tool apart from legal_lookup and legal_search without opening the schema.

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 states when to use it (multiple citations in one call, 1-20) and explicitly when not to: 'Fuzzy resolution of a hard-to-find provision belongs to legal_lookup, not to this tool.' It also clarifies that norm citations require exact match vs decision references that allow relaxed/ambiguous matching, giving the agent a clear decision rule for routing.

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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