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Server Quality Checklist

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  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: one inspects a single page's health, one scans all links on a page, and one compares two URLs. Even where check_page and compare_pages both analyze pages, their roles are separated clearly enough that an agent should not confuse them.

    Naming Consistency5/5

    All tool names follow the same lowercase verb_noun pattern: check_page, check_links, compare_pages. The shared 'check' prefix for two tools is appropriate since they share a similar action family, and compare_pages remains consistent in structure.

    Tool Count5/5

    Three tools is a well-scoped size for a focused deploy-checking server. Each tool covers a distinct real workflow with no redundancy, and the count feels intentional rather than thin or bloated.

    Completeness4/5

    The tool set covers the core post-deploy needs: verifying a single page actually renders, checking linked resources, and comparing environments or releases. A minor gap is the lack of a batch or multi-page smoke-test tool, but agents can work around that by calling check_page multiple times.

  • Average 4.1/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 8 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 MIT License.

  • 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.

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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

  • Behavior5/5

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

    Annotations already mark the tool read-only, idempotent, and non-destructive, and the description adds meaningful behavioral detail: HEAD-first requests with GET fallback so 405s aren't misread, plus default rate limiting to avoid hammering sites. This gives the agent a clear mental model of how requests are made.

    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 no wasted words; purpose comes first, then method, then the politeness guarantee. Every sentence adds 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?

    The description covers purpose, HTTP method details, and rate limiting, which is sufficient for a read-only link checker with annotations. The only gap is the lack of an explicit description of the output shape, but since there is no output schema, an agent might still be unsure what the tool returns.

    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 explains limit, concurrency, and sameHostOnly, covering 80% of parameters; the description adds the notion of a 'capped and rate-limited' default, loosely tied to limit/concurrency. It does not elaborate on timeoutMs or give further parameter-specific guidance, so it stays at 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 opens with 'Read every link on a page and check each one resolves,' which names the exact verb and resource. This clearly separates it from siblings check_page and compare_pages because it targets link resolution rather than single-page or comparative analysis.

    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?

    There is no explicit guidance about when to prefer this tool over check_page or compare_pages, nor any mention of situations where link checking is inappropriate. Usage context is only implied by the title and purpose, so the agent gets no routing help.

    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 readOnlyHint, idempotentHint, and destructiveHint false. The description adds meaningful behavioral context: the tool not only checks HTTP status but also assesses visible text rendering, and warns that a 200 can still be useless. It also lists the extra metadata fields it reports. No contradictions with 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 compact and front-loaded: it opens with the core action and outcome, then the unique rendering insight, then the output list, and ends with the use case. The parenthetical asides are substantive, not filler. Every sentence contributes.

    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 simple 2-parameter, read-only tool with no output schema, the description covers the inputs and the key output aspects. It names the specific fields reported (status, redirect chain, response time, visible text, title, meta, viewport, canonical, og:image, noindex). It doesn't specify the exact output format/type, but that is a minor gap for invocation. The deploy use case rounds it out.

    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?

    Input schema covers 100% of parameters with descriptions: url includes protocol requirement and localhost support, timeoutMs has min/max and default. The description does not add additional parameter-level detail, but the schema already handles that. 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?

    Clear verb 'Fetch' and resource 'URL' with an explicit goal: determine if a page is genuinely working. Distinguishes itself by focusing on visible text rendering, which is a unique angle versus siblings. The description lists concrete output elements (status, redirect chain, response time, etc.), leaving no ambiguity about the tool's scope.

    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?

    Provides explicit timing guidance: 'Use this right after a deploy.' This establishes a clear context for when to invoke. It does not name alternatives or exclusion cases, so not a 5, but the deploy scenario is a strong contextual signal.

    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 establish the read-only, idempotent, non-destructive profile. The description adds useful behavioral context by enumerating exactly what is compared and by explaining how to interpret the result. It does not contradict the annotations and does not need to repeat them.

    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: first sentence states the core function, second gives the primary use case, third provides an interpretation rule. Every sentence adds value with no superfluous detail.

    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 read-only comparison tool, the description covers what the tool does, when to use it, and what output dimensions to expect. It does not mention edge cases like unreachable URLs, but the annotations and simple three-parameter schema reduce the risk of mis-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 already documents urlA and urlB with examples, and the description reinforces their semantic role as staging/production or before/after. However, timeoutMs is not explained in the description beyond schema constraints, and the description adds limited new parameter-level insight beyond the schema.

    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 verb and resource ('Check two URLs and report where they differ') and lists concrete comparison fields such as status, title, canonical, and byte size. The two-URL framing clearly distinguishes this tool from the sibling single-page and link-checking tools.

    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?

    It gives explicit use cases: comparing staging vs production and comparing the same URL before/after release. It also provides an interpretation rule for results ('did my deploy actually land?'), though it does not explicitly mention when to use a sibling tool instead.

    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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