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kimhjort

aria-mcp-cvr-dk

by kimhjort
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation4/5

    Both tools interact with the CVR register, but descriptions clearly differentiate: lookup_company accepts CVR number, name, or phone, while search_companies is specifically for name search and warns it returns only one match. Minor overlap exists since both can search by name, but the descriptions help agents choose correctly.

    Naming Consistency5/5

    Both tool names follow the consistent verb_noun snake_case pattern: lookup_company and search_companies. No deviations or mixed conventions.

    Tool Count3/5

    With only 2 tools, the server feels thin for a dedicated CVR lookup service. While the domain is narrow, a typical minimal set might include more variations (e.g., search by address or industry), so the count is borderline.

    Completeness3/5

    The server covers basic lookup and name search, but lacks other common operations like industry code lookup, paginated search, or filtering. Core functionality is present, but there are notable gaps for a comprehensive company registry tool.

  • Average 4.6/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues 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 is passing

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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?

    No annotations are provided, so the description carries the full burden. It discloses that the tool returns a single best match with a note, and mentions data source and rate limit. However, it does not describe error handling or output format in detail, which would improve transparency.

    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 concise, containing four sentences that each add value: purpose, behavioral note, alternative tool, and data source/limit. No wasted words, and the key information is 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 tool has one parameter, no output schema, and no annotations, the description is fairly complete. It explains the data source, behavior, rate limit, and alternative. It lacks details on the response format (e.g., structure of the match object), but the note about returning 'a note about this behaviour' adds some context.

    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?

    The input schema already provides a clear description for the single parameter 'name' ('Company name (or partial name) to search for.'). The description does not add additional semantic value beyond what the schema provides, so 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 clearly states the tool searches for Danish companies by name in the CVR register, and distinguishes from the sibling tool lookup_company for exact CVR lookups. The verb 'search' and resource 'Danish companies by name' are specific.

    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 explicitly tells when to use this tool (search by name) and when to use the alternative (exact lookup_company). It also notes the behavioral quirk (single best match) and rate limit, providing clear 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 are provided, so the description fully discloses behavior: it states the data source (cvrapi.dk), rate limit (~50 lookups/day), and lists the return fields. This transparency is excellent for a lookup tool.

    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 concise (two sentences) and well-structured. The first sentence states the action and search keys, the second provides usage context and data source. No wasted words.

    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 simple nature of the tool, the description covers all necessary aspects: purpose, usage, return fields, data source, and rate limit. No output schema exists, but the description lists the fields, making it complete.

    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 already describes the 'query' parameter, but the description adds valuable meaning by specifying that it can be a company name, CVR number, or phone number, which is not in the schema 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 action ('Look up'), the resource ('Danish company in CVR'), and the search keys (CVR number, company name, or phone number). It distinguishes itself from the sibling 'search_companies' by being a direct lookup.

    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 clear when-to-use context: 'Use this when you need to identify a company from an invoice, email, or contract.' It gives concrete examples. It does not explicitly state when not to use, but the context is strong enough to infer.

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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