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Server Quality Checklist

67%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: spa_read extracts textual content as Markdown for LLM processing, while spa_screenshot captures visual output as PNG for screenshots. There is no overlap in functionality or ambiguity about which tool to use for a given task.

    Naming Consistency5/5

    Both tools follow a consistent 'spa_' prefix pattern with descriptive suffixes (read, screenshot), indicating they belong to the same domain and operate on SPA pages. The naming is uniform, predictable, and clearly communicates each tool's function.

    Tool Count3/5

    With only 2 tools, the server feels thin for a general-purpose SPA reader domain, as it lacks operations like navigation, interaction simulation, or performance monitoring. However, it covers the core tasks of content extraction and screenshot capture adequately for basic use.

    Completeness3/5

    The tools provide essential read-only capabilities for SPAs (extracting content and screenshots), but there are notable gaps: no ability to interact with pages (e.g., click buttons, fill forms), navigate beyond initial URLs, or handle dynamic content beyond rendering. This limits advanced agent workflows.

  • Average 3.3/5 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 is failing
  • This repository is licensed under MIT License.

  • 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 the full burden of behavioral disclosure. It mentions the headless browser method and PNG output format, but lacks critical details like authentication requirements, rate limits, error conditions, or whether the operation is idempotent. For a complex tool with 8 parameters, this is insufficient.

    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 perfectly concise with two sentences that directly communicate the tool's purpose and method. Every word earns its place with zero redundancy or 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 complex tool with 8 parameters, no annotations, and no output schema, the description is incomplete. It lacks information about return values, error handling, performance characteristics, and operational constraints that would help an agent use it effectively.

    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 no parameter-specific information beyond what's in the schema, maintaining the baseline score of 3 for adequate but not enhanced parameter documentation.

    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 specific action ('Take a screenshot'), target resource ('JavaScript SPA page'), and method ('Uses a headless browser to execute JavaScript and capture the visual output as PNG'). It distinguishes from the sibling tool 'spa_read' by focusing on visual capture rather than content reading.

    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, nor does it mention any prerequisites or exclusions. It simply states what the tool does without contextual usage information.

    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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions using a headless browser and extracting main article content, but lacks critical details such as whether this is a read-only operation, potential performance impacts (e.g., timeouts, resource usage), error handling, or authentication requirements (though headers/cookies parameters hint at this). The description is insufficient for a tool with complex behavior involving browser automation.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is front-loaded with the core purpose in the first sentence and adds implementation detail in the second. Both sentences are relevant and non-redundant, though it could be slightly more structured (e.g., explicitly separating purpose from method). No wasted words, making it efficient for an agent to parse.

    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 tool's complexity (headless browser execution, 6 parameters, no output schema, and no annotations), the description is incomplete. It lacks information on return values (e.g., format of extracted Markdown, error responses), behavioral constraints (e.g., rate limits, side effects), and does not compensate for the absence of annotations. This leaves significant gaps for an agent to use the tool effectively.

    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 no additional parameter semantics beyond what the schema provides, such as explaining how 'waitForSelector' relates to content extraction or typical use cases for cookies/headers. The baseline score of 3 reflects adequate but minimal value added over 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 clearly states the specific action ('Render a JavaScript SPA page and extract its content as LLM-ready Markdown') and distinguishes it from the sibling tool spa_screenshot by focusing on content extraction rather than visual capture. It specifies the method ('Uses a headless browser to execute JavaScript') and the target resource ('SPA page').

    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 SPA pages with JavaScript-rendered content, but does not explicitly state when to use this tool versus alternatives like spa_screenshot or other non-SPA reading tools. No exclusions or prerequisites are mentioned, leaving the agent to infer the context from the tool name and description alone.

    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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