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LegalEngineering

sk-registers-mcp

by LegalEngineering
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: 'detail_subjektu' retrieves detailed information for a specific legal entity by IČO, while 'hladaj_subjekt' searches for entities across multiple criteria. There is no overlap in functionality that would cause confusion.

    Naming Consistency5/5

    Both tool names follow a consistent Slovak verb_noun pattern ('detail_subjektu' and 'hladaj_subjekt'), using underscores and descriptive action-object naming. The naming is uniform and predictable across the set.

    Tool Count3/5

    With only 2 tools, the server feels minimal for a legal entity registry domain. While it covers basic lookup and search, typical CRUD operations or additional registry functions might be expected, making it borderline thin for the apparent scope.

    Completeness3/5

    The tools provide search and detail retrieval, covering core lookup needs, but there are notable gaps such as update, delete, or creation operations for legal entities. The surface is functional but incomplete for full lifecycle management in a registry context.

  • Average 4.1/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 0 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

  • 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.

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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

  • Behavior3/5

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

    With no annotations provided, the description carries the full burden. It clearly describes what data is returned (statutory representatives, partners, business activities, legal form, address) and the effect of the 'historia' parameter on historical records. However, it doesn't mention error conditions, rate limits, authentication requirements, or data freshness.

    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?

    Three sentences with zero waste. The first states the core purpose, the second lists returned data fields, and the third explains the optional parameter's effect. Each sentence earns its place by adding distinct value.

    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 moderate complexity (2 parameters, no annotations, but has output schema), the description is reasonably complete. It explains what data is returned and parameter effects. The existence of an output schema reduces the need to describe return values in detail. Minor gaps include lack of error handling information.

    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 0% schema description coverage, the description must compensate. It explains both parameters: 'ico' as the identifier for the legal entity and 'historia' as a boolean controlling whether historical records are included. This adds meaningful context beyond the bare schema, though it doesn't specify format requirements for 'ico'.

    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 specific action ('Vráti kompletný detail' - returns complete detail), the resource ('právnickej osoby z RPO' - legal entity from RPO), and the key identifier ('podľa IČO' - by IČO). It distinguishes from the sibling tool 'hladaj_subjekt' (search subject) by focusing on detailed retrieval rather than searching.

    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 when detailed information about a specific legal entity is needed, but doesn't explicitly state when to use this versus the sibling 'hladaj_subjekt' or any other alternatives. It mentions the 'historia' parameter for historical records, which provides some contextual guidance.

    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 provided, the description carries full burden. It discloses that the tool searches a registry aggregating data from ~70 source registries, which adds useful context about data sources. However, it doesn't mention behavioral traits like rate limits, authentication needs, response format, or pagination behavior that would be important for a 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.

    Conciseness5/5

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

    The description is efficiently structured in three sentences: purpose statement, parameter requirement, and data source context. Every sentence adds value with zero wasted words. It's appropriately sized for the tool's complexity.

    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 an output schema (which handles return values), no annotations, and 0% schema description coverage, the description does well. It covers purpose, parameter relationships, and data source context. The main gap is lack of behavioral details like rate limits or authentication, but the output schema reduces the completeness burden.

    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 0% schema description coverage and 4 parameters, the description must compensate. It explains the purpose of 3 parameters (nazov, ico, obec) and their relationship ('aspoň jeden z parametrov nazov/ico musí byť zadaný'). It doesn't mention the 'iba_aktivne' parameter, but provides good semantic context for the majority of parameters.

    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's purpose: 'Hľadá právnické osoby v RPO (Register právnických osôb)' - it searches for legal entities in a specific registry. It specifies the search criteria (by name, IČO, or municipality) and distinguishes from sibling 'detail_subjektu' by being a search function rather than a detail view.

    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 usage context: 'Aspoň jeden z parametrov nazov/ico musí byť zadaný' specifies a mandatory parameter requirement. It doesn't explicitly mention when to use vs. the sibling tool 'detail_subjektu', but the search vs. detail distinction is implied. No explicit exclusions or alternatives are stated.

    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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