handelsregister-mcp

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool has a distinct purpose: search_company for searching, get_company for best-match lookup, fetch_document for general extracts, fetch_filed_document for specific filed documents, list_filed_documents for categories, get_shareholders for structured shareholder data, and rate_limit_status for budget info. No two tools do the same thing.
Naming Consistency4/5
Most tools follow a verb_noun pattern (fetch_, get_, list_, search_). The naming is all lowercase with underscores, but rate_limit_status is a noun_noun_noun exception. Overall consistent and readable.
Tool Count5/5
7 tools cover the German commercial register domain well: search, company details, multiple document retrieval types, filed document listing and download, and rate limit monitoring. The count feels well-scoped without unnecessary tools.
Completeness5/5
The tool set covers all major read operations: searching for companies, retrieving current/historical extracts, filed documents (including shareholder lists with structured output). No obvious gaps like missing document types or inability to fetch by register number.
Average 3.9/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 11 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under Apache 2.0.
This repository includes a README.md file.
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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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, and the description only notes grouping by category. It lacks details on error handling, pagination, or read-only nature, leaving behavioral traits largely unspecified.
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?
Two sentences efficiently convey purpose and usage of results, though adding parameter details would improve conciseness without bloat.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 3 parameters with 0% schema description, no output schema, and no annotations, the description is insufficient. It fails to explain parameters or return structure, leaving the agent underinformed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% and the description gives no explanation for 'company', 'match', or 'result_index'. The agent receives no guidance on how to provide valid input beyond the schema structure.
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 'List the documents filed for a company' and distinguishes from sibling fetch tools by indicating this is for listing, not downloading.
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?
Explicitly tells when to use this tool (listing) and directs to 'fetch_filed_document' or 'get_shareholders' for downloading, effectively differentiating from siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries burden. It discloses download behavior, return values (local path, text), and special handling for shareholder lists (table parsing). However, it does not mention auth needs, rate limits, or side effects (though presumably read-only). Adequate but not comprehensive.
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 two short paragraphs. First sentence states core purpose, second adds behavioral details. No extraneous text. Front-loaded and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description explains return values. But with 4 parameters and low schema coverage, leaving three unexplained is a significant gap. The relationship to list_filed_documents is helpful, but overall incomplete for parameter usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so description must compensate. It explains the category parameter in detail (substring matching, examples), but does not describe company, which (default 'latest'), or match (default 'exact'). Only 1 of 4 parameters gets meaningful explanation.
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 downloads a filed document from the DK document register, specifying verb, resource, and context. It distinguishes from siblings like list_filed_documents (listing vs downloading) and fetch_document (different register implied).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides context on category matching (case-insensitive substring) and gives examples, but does not explicitly state when to use this tool vs alternatives like fetch_document or list_filed_documents. Missing 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.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses that document retrieval is 'the most fragile part of the portal flow' and that errors explain what happened. It also outlines the steps (search, pick index, download) and document types. This adds behavioral context beyond a simple read operation, though it could mention if auth or rate limits apply.
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 well-structured with front-loaded purpose, a clear step-by-step process, and a bulleted list of document types. It is informative without being overly verbose, earning its sentences. Minor redundancy in the first sentence (repeats 'document/extract') but overall efficient.
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 no output schema, the description explains return fields (path, content_type, size_bytes, text) and addresses fragility. It covers the search-to-download flow and document types. For a tool with 4 parameters and a moderate complexity, this provides sufficient completeness, though sibling differentiation is missing.
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 0%, so the description must compensate. It adds meaning to 'keywords' (used for search), 'result_index' (picked from hits), and 'document_type' (lists options), but does not explain the 'match' parameter. The description provides context but is not fully comprehensive for all 4 parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves a register document/extract for a company and returns its text. It specifies the process: runs a search, picks a result index, downloads the document type. While the purpose is specific and distinct from generic document fetching, it does not explicitly differentiate from the sibling tool 'fetch_filed_document', which overlaps in functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context through the document types and fragility warning, but it does not explicitly state when to use this tool versus alternatives like fetch_filed_document. No exclusions or prerequisites are mentioned, leaving the agent to infer use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full burden. It discloses a read-only status check but omits details such as whether the budget resets, is per user or API key, or how to interpret the response. Minimal but not misleading.
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?
Single sentence, front-loaded with key information, no unnecessary words. Perfectly concise.
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 no output schema and no annotations, the description explains the core purpose but lacks details like budget reset period or response structure. Adequate for a simple status tool but incomplete for full agent decision-making.
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?
The input schema has zero parameters, so schema coverage is 100%. The description adds no extra parameter information, but the baseline is 4 due to no parameters needing elaboration.
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 verb 'Report' and the resource 'remaining handelsregister.de request budget for this hour', which is specific and distinct from sibling tools that fetch documents or company info.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not provide explicit guidance on when to use this tool versus alternatives, nor does it mention prerequisites or best practices like checking rate limits before making other requests. The usage is implied but not clarified.
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?
The description details the search logic (exact then fuzzy/phonetic) and the two possible outcomes (direct match or suggestions with found: false). Since no annotations are provided, this transparency is valuable. However, it does not disclose potential side effects or authentication needs, but for a read-only lookup this is sufficient.
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 two sentences long, front-loaded with the core purpose, and efficiently covers the process and outcomes without extraneous detail.
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 single parameter and lack of output schema, the description adequately explains the return behavior (company vs. suggestions). However, it could mention the structure of suggestions or the company object to be fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema coverage, the description fully compensates by explaining that the 'name' parameter is the company name and describing how it is used in the matching process. It adds context beyond the schema by mentioning fuzzy and phonetic search.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it looks up a company by name and returns the best match or suggestions. It specifies the verb and resource, and the fallback logic helps distinguish it from potentially broader sibling tools like search_company. However, it does not explicitly differentiate from siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives. The description implies use when you have a specific company name, but it does not mention when to avoid it or when to use sibling tools like search_company instead.
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, the description fully discloses behavioral traits: resolution strategy (exact→fuzzy→phonetic), ambiguity handling (returns suggestions), extraction method (coordinate-aware parser and text heuristic), confidence reporting, and fallback behavior (raw_text and PDF path on low confidence). No contradictions with annotations exist.
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 well-structured with a clear opening sentence, followed by layered details. It is front-loaded with the main purpose. While slightly lengthy, every sentence adds value and context, making it informative without being verbose.
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?
Given the two parameters and no output schema, the description comprehensively covers input semantics, multiple extraction methods, confidence handling, and output field list. It anticipates edge cases (ambiguous names, low confidence) and provides fallback instructions, leaving little ambiguity for the agent.
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?
The schema coverage is 0%, but the description adds meaning beyond the schema: 'company' is the company name to resolve, and 'which' accepts 'latest' (default) or 'oldest'. It explains the resolution process for company. However, it does not specify that 'which' is limited to those two values, nor does it provide format guidance for the company name.
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 starts with a precise verb-resource pair ('Retrieve a company's shareholders') and specifies the output format ('structured table'). It distinguishes itself from siblings by noting that shareholders are NOT in the register extract for a GmbH/UG, implying this tool is the authoritative source and different from get_company.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides guidance on when to use this tool versus alternatives (e.g., 'Shareholders are NOT in the register extract for a GmbH/UG — this filed list is the authoritative source') and explains the resolution process for company names. However, it does not explicitly state when not to use it or compare to closely related siblings like fetch_filed_document.
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 fully discloses behavioral details: wildcard support, matching algorithms, phonetic matching, page size limit (~100), and hourly request budget. There is 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a concise intro, followed by clear parameter bullet points, and a summary of the return value. Every sentence adds value with no redundancy.
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?
Despite no output schema, the description thoroughly explains the return structure (dict with query echo, count, hourly budget, and results with detailed fields). It also notes the connection to fetch_document, making it complete for a search tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% coverage, so the description must compensate. It provides exhaustive meaning for all 4 parameters: examples of wildcards, match options (all/min/exact), similar flag explanation, and max_results context about page size.
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 specifies the verb 'Search' and the resource 'German commercial register (Handelsregister) for companies', providing a precise and unambiguous purpose that distinguishes it from sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description offers clear guidance on parameter usage (keywords, match modes, similar, max_results) and mentions cross-reference to fetch_document, but lacks explicit when-not-to-use or alternative tool recommendations for queries that might be better served by get_company.
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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