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Server Quality Checklist

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  • Latest release: v0.3.3

  • Disambiguation5/5

    Each tool targets a clearly distinct function: metadata retrieval, full-text retrieval, and coverage/gap declaration. There is no overlap or ambiguity between them.

    Naming Consistency4/5

    Two tools follow the mt_get_* verb_noun pattern, while mt_coverage uses only a noun. This is a minor deviation but the shared mt_ prefix and clear semantics keep the set predictable.

    Tool Count5/5

    Three tools is small but perfectly suited to this connector's narrow scope: retrieving Maltese legal documents by ELI and declaring coverage. Each tool has a distinct, necessary role.

    Completeness4/5

    The tool set covers metadata retrieval, full-text retrieval, and explicit gap disclosure, which is complete for a read-only connector. A search/list capability is absent, but the coverage tool proactively mitigates that limitation.

  • Average 4.2/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 16 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.

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      ]
    }

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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 and idempotentHint, establishing safe read behavior. The description adds the source ('official PDF') and scope ('full text'), which gives minor context beyond safety. However, it does not disclose error behavior, language output, or any caching aspects, but the annotation coverage lowers the burden.

    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?

    A single, front-loaded sentence that conveys the core functionality without wasted words. It is appropriately sized for the tool's simplicity.

    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?

    The tool is simple, has an output schema, and its annotations cover safety. The description communicates the source and output type sufficiently. It lacks explicit alternative guidance, but that is more a usage-guideline concern; overall it is complete enough for invocation.

    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 schema covers both parameters with descriptive text (eli coordinate, lang values), and the description's mention of 'by ELI coordinate' reinforces eli's role. Since schema description coverage is 100%, the description adds little beyond schema, meriting the baseline 3.

    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 fetches the full text of a Maltese document using an ELI coordinate, specifying the source as the official PDF. The verb 'Fetch' and resource 'full text' make the purpose unambiguous, and the sibling tool name (mt_get_act) suggests a different focus, providing implicit differentiation.

    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 for retrieving document text but provides no explicit when-to-use or alternative guidance. It does not explain when to prefer this over mt_get_act, leaving the agent to infer which tool matches the user's intent. No exclusions or context is given beyond the basic purpose.

    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 readOnly, openWorld, idempotent, and non-destructive behavior, so the bar is lower. The description adds that this returns 'metadata' (not full text), which is useful behavioral context, but it does not disclose error handling or edge cases. Minimal additional 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?

    A single, front-loaded sentence that immediately states the tool's purpose and method. No filler or redundancy; every word earns its place.

    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 read-only tool with two well-documented parameters, an output schema, and strong annotations, this description is sufficient. It clearly states the core function, and the schema and annotations cover the remaining 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?

    Schema description coverage is 100%, with clear explanations for both 'eli' and 'lang'. The description adds no parameter-specific meaning beyond what the schema already 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 uses a specific verb ('Fetch') and resource ('Maltese document metadata') and specifies the method ('by ELI coordinate'). It clearly distinguishes from the sibling mt_get_text, which likely retrieves full text rather than metadata.

    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 metadata retrieval, separate from the sibling text tool, but it does not explicitly state when to use this tool over alternatives or provide exclusions. Clear context is present, but no direct compare/contrast.

    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?

    Beyond the read-only/idempotent annotations, the description warns that an empty result may reflect a connector gap rather than an absence in the law, and that gaps include fallback pointers. This is valuable behavioral context that annotations alone do not convey.

    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 compact and front-loaded, leading with the tool's core declaration, then the when-to-use guidance, then the return shape. No sentence is redundant.

    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 parameterless, read-only tool with an output schema, the description fully covers when to call it, what it returns, and an important caveat about empty results. Nothing needed to invoke it correctly is missing.

    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 tool has zero parameters, so the empty input schema needs no description augmentation. The baseline of 4 applies because there is nothing for the description to add about 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 states a specific purpose: declaring coverage scope, sourcing, and exclusions for the connector. This clearly distinguishes mt_coverage from the sibling content-retrieval tools mt_get_act and mt_get_text, which retrieve legal text rather than describe coverage gaps.

    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 gives explicit triggers: call before telling a user the law does not contain something, and whenever a search returns empty. It does not name sibling alternatives directly, but the fallback routing inside each gap provides enough context to decide when this tool is needed.

    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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  • Evaluate tool definition quality.

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