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matematicsolutions

hr-eli-mcp

by matematicsolutions
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Python
Remote

Server Quality Checklist

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

  • Latest release: v0.2.1

  • Disambiguation5/5

    Each tool serves a distinct purpose: listing documents in an issue, fetching metadata, and fetching full text. While get_act and get_text both use ELI coordinates, they clearly differ in output (metadata vs. text), so there is no ambiguity.

    Naming Consistency5/5

    All tool names follow a consistent 'hr_verb_noun' pattern with snake_case. The verbs (list/get) and nouns (issue/act/text) are meaningful and predictable.

    Tool Count4/5

    3 tools is a slim but reasonable set for a focused ELI document access server. It avoids bloat, though it might feel minimal for broader legal research tasks.

    Completeness4/5

    Core reading workflows are covered: listing an issue, retrieving metadata, and retrieving text. A notable gap is lack of search or discovery tools (e.g., finding acts by query), but the core ELI-based access is functional.

  • Average 4/5 across 3 of 3 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
    • Last stable release on
    • 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.

  • 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.

  • This repository includes a glama.json configuration file.

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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

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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?

    Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds no extra behavioral context beyond the basic listing function, such as pagination, result limits, or authentication requirements. It is consistent with annotations but doesn't go further.

    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 a single, front-loaded sentence that clearly states the tool's purpose without unnecessary words or repetition. It is concise and immediately understandable.

    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?

    The tool is simple, has a clear output schema, rich annotations, and a concise description. No critical information is missing for an agent to select and invoke this correctly; the description and schema together cover the necessary 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 covers both parameters (year and issue) with descriptive examples in comments, achieving 100% schema description coverage. The description itself adds no parameter detail beyond what the schema already provides, so baseline 3 applies.

    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 uses a specific verb ('List') with a clear resource ('documents published in a Narodne novine gazette issue'). This distinguishes it from sibling tools (hr_get_act, hr_get_text) which fetch specific items rather than listing all documents in an issue.

    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 provides no guidance on when to use this tool versus alternatives. It doesn't mention that the user should first list documents before fetching specific acts or texts, nor does it give any context about the typical workflow or exclusions.

    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?

    Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. The description's 'Fetch' aligns with these hints but adds no extra behavioral detail such as authentication requirements or potential errors, so the description adds no value beyond 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 a single sentence that states the action and object succinctly, with no wasted words or redundant details. It is excellently front-loaded.

    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 complete input schema, rich safety annotations, and an output schema (not shown but indicated), the tool is fully specified. The short description is sufficient for a simple document-fetch operation.

    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% with each parameter (year, issue, doc) having clear descriptions and examples. The description itself does not add further parameter meaning, but the schema already carries the full load, meeting the baseline.

    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 uses a specific verb 'Fetch' and names the resource: 'full official HTML text of a document by ELI coordinate.' This clearly distinguishes it from siblings like hr_list_issue (listing issues) and hr_get_act (retrieving acts specifically).

    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?

    No usage guidance is provided beyond the tool's basic function. It does not mention alternatives like hr_get_act or state when to prefer this tool, so the usage context is only implied.

    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?

    Annotations already declare readOnly, idempotent, openWorld, and not destructive. The description adds that this returns 'metadata' rather than the full text, which is a behavioral distinction beyond the annotations. No contradiction found.

    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 a single, concise sentence that front-loads the action and resource, with zero 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?

    For a simple 3-parameter metadata fetch with a full output schema and clear annotations, the description provides enough context. It clarifies the domain (ELI) and the distinction between metadata and text, making the tool complete for an agent to invoke correctly.

    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 provides 100% coverage with descriptions and examples for all three parameters. The description merely restates them as 'year + issue + doc' without adding any extra meaning, so it meets the baseline for high schema coverage.

    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 uses a specific verb ('Fetch'), names the resource ('document metadata'), and specifies the unique identifier type ('ELI coordinate'), which clearly distinguishes it from siblings like hr_get_text (which retrieves text) and hr_list_issue (which lists issues).

    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 implies the tool is for retrieving metadata by a precise coordinate, contrasting with siblings that handle listing or text retrieval. However, it does not explicitly state when to use this tool over alternatives or call out exclusions, so it stops short of a 5.

    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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