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self-tech-labs

Online Kommentar MCP Server

by self-tech-labs
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TypeScript
Remote

Server Quality Checklist

50%
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: one retrieves a specific commentary by ID, while the other searches for commentaries based on queries and filters. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with snake_case naming: get_commentary_by_id and search_commentaries. The naming is predictable and readable throughout.

    Tool Count2/5

    With only two tools, the server feels thin for a legal commentary domain. It lacks essential operations like creating, updating, or deleting commentaries, which limits its utility and scope.

    Completeness2/5

    The tool surface is severely incomplete for a legal commentary server. It only provides retrieval and search capabilities, missing core CRUD operations (create, update, delete) and other lifecycle management tools, leading to significant gaps in functionality.

  • Average 2.9/5 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

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  • 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?

    With no annotations provided, the description carries full burden for behavioral disclosure. It states this is a retrieval operation, implying read-only behavior, but doesn't address permissions, error conditions, rate limits, or what happens if the ID doesn't exist. Significant behavioral context is missing.

    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, efficient sentence with zero wasted words. It's appropriately sized for a simple retrieval tool and gets straight to the point without unnecessary elaboration.

    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?

    For a retrieval tool with no annotations and no output schema, the description is insufficient. It doesn't explain what a 'commentary' is, what format it returns, error handling, or how this differs from the sibling search tool. More context is needed for proper agent understanding.

    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 description coverage is 100%, so the schema already fully documents the single 'id' parameter. The description doesn't add any additional parameter semantics beyond what's in the schema, meeting the baseline for high coverage.

    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 verb ('retrieves') and resource ('a specific commentary'), making the purpose unambiguous. However, it doesn't explicitly differentiate from the sibling 'search_commentaries' tool, which would be needed for a perfect score.

    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 guidance is provided about when to use this tool versus the sibling 'search_commentaries' tool. The description doesn't mention prerequisites, alternatives, or contextual constraints, leaving the agent without usage direction.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior2/5

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

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'searches' but doesn't clarify if this is a read-only operation, what the expected response format is, whether there are rate limits, authentication requirements, or pagination behavior beyond the 'page' parameter. This leaves significant gaps in understanding how the tool behaves in practice.

    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, efficient sentence that directly states the tool's function without unnecessary words. It's front-loaded with the core action and resource, making it easy to parse quickly. Every part of the sentence contributes to understanding the tool's purpose.

    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 the complexity of a search tool with 5 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what a 'legal commentary' is, the search scope, result format, or behavioral aspects like error handling. This leaves the agent with insufficient context to use the tool effectively beyond basic parameter input.

    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 description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value by mentioning 'filters' generically, but doesn't elaborate on specific parameters like 'legislative_act' or 'sort' beyond what the schema provides. This meets the baseline for high schema coverage, but doesn't enhance parameter understanding.

    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 action ('searches for') and resource ('legal commentaries'), making the purpose understandable. It distinguishes from the sibling tool 'get_commentary_by_id' by indicating this is a search operation rather than retrieval by specific ID. However, it doesn't specify what constitutes a 'legal commentary' or the search scope beyond 'based on a query and filters,' which keeps it from being fully specific.

    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 mentions 'filters' but doesn't specify which filters are available or when to apply them. There's no mention of prerequisites, limitations, or comparison to the sibling tool 'get_commentary_by_id,' leaving the agent without clear usage context.

    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 the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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