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Server Quality Checklist

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  • Latest release: v0.0.1

  • Disambiguation5/5

    The two tools have clearly distinct inputs and purposes: verify_url checks a given URL/domain against the official record, while get_official_url retrieves the official URL from a product or company name. No ambiguity exists between them.

    Naming Consistency5/5

    Both tool names follow the same verb_noun pattern with snake_case: verify_url and get_official_url. The verbs ('verify', 'get') match the respective actions, creating a predictable and consistent naming convention.

    Tool Count3/5

    With only two tools, the server feels slightly thin even though both tools cover the core domain of official URL verification and lookup. Each tool earns its place, but the small count leaves little room for broader utility or edge-case coverage.

    Completeness5/5

    The two tools cover the entire primary workflow: given a name, look up the official URL; given a candidate URL, verify its official status. There are no obvious gaps for a read-only service focused on official website verification.

  • 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
    • No commit activity data available
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing
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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?

    With no annotations provided, the description carries the full disclosure burden, and it does well: it reveals that results are 'verified', that evidence accompanies the URLs, and that it has an explicit failure mode ('says the site could not be confirmed'). It stops short of disclosing data freshness, rate limits, or whether it performs live web lookups, but the core behavioral traits are covered.

    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?

    Two sentences with zero filler. The primary action is front-loaded in the first sentence, and the second sentence efficiently covers both the evidence behavior and the not-confirmed fallback.

    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?

    For a single-parameter tool with full schema coverage, the description is nearly complete: it states the action, the return payload (verified URLs plus evidence), and the negative case. Minor gaps remain — no explicit routing to verify_url for URL-shaped inputs and no detail on the evidence format — but nothing an agent needs to make the call is missing.

    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%, and the schema already documents the parameter with an illustrative example ('Ollama', 'Claude Code'). The description echoes 'by name' but adds no format or syntax detail beyond the schema, so the 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 ('look up'), a specific resource ('verified official website'), and a precise scope ('software product, AI tool, or company by name'). The 'by name' phrasing cleanly differentiates it from the sibling verify_url, which presumably checks a URL rather than finding one.

    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 the use case: fetching a known product/tool/company's official site by name. However, it never explicitly contrasts this with verify_url or tells the agent when to choose one over the other (e.g., 'if you already have a URL, use verify_url instead'). The guidance is present only by inference.

    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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It does well by specifying the exact return categories, noting that not_official comes with the real official domains, and clarifying that the tool judges ownership only and never safety. Some details like error handling or data source are absent, but the core behavior is transparent.

    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 three short sentences with no filler. It front-loads the primary action, then efficiently lists return values, and closes with a one-line scope boundary. Every sentence contributes useful information.

    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 one-parameter tool with no output schema, the description is complete: it tells the agent what input to provide, what the tool decides, what outputs are possible, and what the tool intentionally does not do. No additional context is needed to invoke it 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?

    Schema description coverage is 100% for the single parameter, and the schema already explains that url accepts a URL or bare domain with an example. The description adds no new semantic detail beyond restating 'URL or domain,' so it meets the baseline but does not elevate it.

    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 ('Check whether') with a clear resource (URL or domain) and defines the exact judgment being made: verified official website of a software product, AI tool, or company. It also lists the possible return categories, making the tool's purpose unambiguous. It is readily distinguished from the sibling get_official_url, which conceptually retrieves a canonical URL rather than verifying a given one.

    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 when to use the tool—whenever ownership verification of a URL/domain is needed—and explicitly excludes safety judgments. However, it does not mention the sibling tool get_official_url or state when to prefer one tool over the other, so the substitution/alternative guidance is only implicit.

    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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