Rechtsinformationen Bund DE MCP Server

Server Quality Checklist

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Latest release: v1.4.0
Disambiguation5/5
Each tool has a clearly distinct purpose with minimal overlap. intelligente_rechtssuche is the primary search for both legislation and case law, while deutsche_gesetze_suchen and rechtsprechung_suchen are specialized for legislation-only and case-law-only searches respectively. gesetz_per_abkuerzung_abrufen provides direct abbreviation lookup, and dokument_details_abrufen handles document retrieval. The descriptions explicitly guide when to use each tool, preventing confusion.
Naming Consistency2/5
Naming is inconsistent with mixed conventions. intelligente_rechtssuche uses snake_case, but gesetz_per_abkuerzung_abrufen mixes snake_case with German words and abbreviations. deutsche_gesetze_suchen and rechtsprechung_suchen follow snake_case, while dokument_details_abrufen uses a different verb style (abrufen vs suchen). There's no uniform pattern across all tools, making them less predictable.
Tool Count5/5
Five tools is well-scoped for a legal information server. It covers the core workflows: primary search, specialized searches, direct lookup, and document retrieval. Each tool serves a specific function without redundancy, and the count is manageable for agents to understand and use effectively.
Completeness4/5
The tool set covers the domain comprehensively with minor gaps. It supports search (both general and specialized), direct lookup, and document retrieval for German legal information. However, there's no explicit tool for browsing or listing laws/cases by metadata (e.g., by date or court without a search term), which could be a minor limitation for some agent workflows.
Average 4.5/5 across 5 of 5 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 1 of 1 community issues answered or closed in the last 6 months
- 2 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description carries the full burden. It discloses important behavioral traits including: 403 Forbidden errors may occur, not all historical versions are accessible via API, and it accepts three different input formats (full URLs, partial paths, or ELI/ECLI identifiers). It could be improved by describing the return value structure or rate limits, but the error condition disclosure is valuable.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear headers, but contains redundancy between sections. The 'Parameters' section largely repeats information already in the schema, and the 'Usage Priority' section repeats guidance from 'When to use.' The markdown formatting and emoji add visual structure but also verbosity for an AI agent. Every sentence is relevant, but some sections are duplicative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that no output schema exists and no annotations are provided, the description should ideally explain the return structure for the three format options (JSON, HTML, XML). While it mentions format options, it doesn't describe what the successful response contains or its structure. The error handling (403) is documented, but the success case remains opaque.
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?
With 100% schema description coverage, the baseline is 3. The description adds significant value by providing concrete examples of the three accepted documentId formats (full URLs, partial paths, ELI/ECLI identifiers) and explaining that documentId corresponds to the '@id' field from search results. It also clarifies when to use each format option (HTML for readable, JSON for structured data).
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 explicitly states the tool 'Retrieves complete document content from rechtsinformationen.bund.de' and specifies it works with 'both legislation and case law documents.' It clearly distinguishes itself from sibling search tools by specifying this is for retrieving full text when you already have a specific document identifier, versus searching.
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?
Contains explicit 'When to use' and 'When NOT to use' sections with specific guidance. Explicitly names the sibling alternative 'intelligente_rechtssuche' for initial searches. Provides a clear workflow: 'Search first → Get results → Use this tool for full text.' This is exactly the type of explicit when/when-not guidance that prevents tool selection errors.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It successfully explains the data source (rechtsinformationen.bund.de), result format (ECLI-based URLs), and URL construction pattern with concrete examples. It does not mention rate limits or authentication requirements, leaving minor gaps for a read-only search tool.
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 uses clear markdown structure with headers and bullet points that make it scannable. While lengthy, every section serves a distinct purpose: scope definition, technical details (URL construction), usage guidance, domain reference (court list), and parameter summary. The 'SECONDARY TOOL' label is appropriately front-loaded.
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?
Given the lack of output schema, the description adequately covers the tool's purpose, data source, parameter semantics (including domain-specific court codes), and result URL format. It could be improved by describing the actual result structure/fields returned, but the ECLI URL explanation provides sufficient context for a search tool.
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%, establishing a baseline of 3. The description adds significant domain value by listing common court abbreviations (BGH, BVerfG, etc.) and clarifying document types. However, it contradicts the schema regarding the default value for 'limit' (description states 10, schema specifies 5), which creates ambiguity for the agent.
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 clearly identifies the tool as searching German court decisions (Rechtsprechung) from federal courts via rechtsinformationen.bund.de. It explicitly distinguishes itself from siblings by labeling itself as a 'SECONDARY TOOL' and stating it should be used after 'intelligente_rechtssuche' for initial queries, making the specific scope unambiguous.
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?
Provides explicit 'When to use' criteria with four specific scenarios (follow-up searches, case-law-only results, specific court filtering, document types). The 'Usage Priority' section gives clear routing instructions: 'For initial queries → Use intelligente_rechtssuche first' and 'For court-specific searches → Use this tool', directly addressing sibling tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full disclosure burden and delivers substantial behavioral context: extensive database coverage limitations (GG not available, SGB I-VIII missing), unreliable date filter warnings, and URL construction format for results. Minor gap: no mention of rate limits, authentication needs, or empty result handling.
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?
Appropriately structured with clear markdown headers (What this tool searches, When to use, Limitations). The 'SECONDARY TOOL' flag is front-loaded. While lengthy, every section serves a purpose—particularly the coverage limitations and URL construction which compensate for missing output schema. No significant waste.
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?
Given the tool's complexity (legal search with database gaps) and lack of output schema, the description is comprehensive. It explains result URL formats, documents critical coverage gaps (missing laws), and provides sibling tool relationships. Adequate for correct invocation despite no return value documentation.
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?
While schema coverage is 100%, the description adds crucial usage context beyond the schema: it warns that temporalCoverageFrom/To 'are unreliable' and advises 'DON'T use date filters' for amendment questions. It aggregates parameter information with usage scenarios, adding value over raw schema definitions.
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 explicitly states it searches the federal legislation database at rechtsinformationen.bund.de for laws, ordinances, and administrative provisions. It clearly distinguishes itself from siblings by specifying it is for 'legislation-only results (excludes case law)' and contrasts with 'intelligente_rechtssuche' in the Usage Priority section.
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?
Explicit 'When to use' section with checkmarks identifies it as a 'follow-up' tool after intelligente_rechtssuche. The 'Usage Priority' section explicitly states 'For initial queries → Use intelligente_rechtssuche first.' It also warns against using date filters for amendment questions, providing clear when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full behavioral disclosure burden and succeeds comprehensively. It discloses critical coverage gaps ('Grundgesetz (GG): NOT in database', 'SGB I-VIII: NOT in database'), failure modes ('lookup will fail or return wrong law'), and describes return values (ELI identifier, version dates, table of contents, URLs) since no output schema exists.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
While well-structured with clear markdown sections, the description is excessively verbose with emoji decoration (📖, ✅, ❌, ⚠️) and repetitive examples. The coverage limitations and usage guidelines are critical and earn their place, but the overall length could be reduced by 30-40% without losing essential information.
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?
Comprehensive for a single-parameter lookup tool with 4 siblings and no output schema. Essential domain-specific constraints (database coverage gaps for major laws like GG) are explicitly documented. Return values, example usage, and tool selection priority are all clearly explained, leaving no critical gaps.
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?
Input schema has 100% description coverage (the 'abbreviation' parameter is documented), establishing baseline 3. The description adds valuable domain context by emphasizing 'Standard German law abbreviation' (implying official abbreviations required) and marking it as '(required)', providing semantic clarity beyond the schema's basic description.
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 clearly states the tool performs 'direct lookup of German federal laws by standard abbreviations' and 'bypasses semantic search for exact law retrieval.' It explicitly differentiates from sibling tools like 'intelligente_rechtssuche' (for broad research) and 'rechtsprechung_suchen' (for court decisions), establishing a clear, distinct purpose.
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?
Contains explicit 'When to use' and 'When NOT to use' sections with specific sibling alternatives named: 'use intelligente_rechtssuche' for broad research, 'use rechtsprechung_suchen' for court decisions, and 'use deutsche_gesetze_suchen' for full-text search. Also includes 'Usage Priority' guidance stating 'For exact law lookup by abbreviation → Use this tool FIRST'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full disclosure burden and excels: details database coverage limitations (GG and SGB I-VIII not available), explains internal search mechanics (Fuse.js fuzzy search vs ML embeddings), discloses FRBR data model structure, and clarifies which metadata requires follow-up calls vs immediate availability.
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?
Extremely long but well-structured with front-loaded critical information (PRIMARY TOOL warning). Uses markdown formatting effectively for scannable sections. While verbose, every section serves a distinct purpose (coverage gaps, data model, agent responsibilities) necessary for this complex legal domain. Minor deduction for potential tightening of redundant 'Intelligent Features' vs 'What this tool does NOT do' sections.
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?
No output schema exists, so description must explain return values. Comprehensive explanation of FRBR model (Work/Expression/Manifestation), immediate vs follow-up metadata availability, and result structure (HTML links, JSON-LD) fully compensates for missing output schema. Completeness is excellent given the tool's complexity and sibling coordination requirements.
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 has 100% coverage (baseline 3). Description adds value by providing concrete query examples (§ 15 BEEG, Elternzeit), explaining the threshold's practical effect (lower = more lenient matching), and advising on search strategies (multiple variations, synonyms). Does not reach 5 because schema already covers technical definitions comprehensively.
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?
Description explicitly states the tool performs full-text search across rechtsinformationen.bund.de for both legislation and case law. It clearly distinguishes itself from siblings by labeling itself as 'PRIMARY TOOL' and explicitly stating when to use specialized follow-up tools (deutsche_gesetze_suchen, rechtsprechung_suchen) instead.
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?
Provides explicit when-to-use guidance ('ALWAYS USE THIS FIRST for ANY German legal question') and comprehensive when-not-to-use guidance via 'What this tool does NOT do' section and 'Usage Pattern' numbered list. Clearly maps the workflow from this tool to sibling tools for follow-up actions.
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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