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xiaobenyang-com

Read-Website

by xiaobenyang-com
OverviewSchemaRelated ServersScoreDiscussions
TypeScript

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined as web content extraction, making it impossible for an agent to misselect between non-existent alternatives.

    Naming Consistency5/5

    The single tool name 'read_website' follows a clear verb_noun pattern, and with no other tools present, there is no inconsistency to evaluate. The naming is straightforward and predictable for this minimal set.

    Tool Count2/5

    One tool is too few for a server named 'Read-Website', as it suggests a narrow scope that might limit functionality. While the tool is well-described, a single tool often feels insufficient for robust web content interactions, such as handling errors, managing sessions, or providing additional utilities like summarization or filtering.

    Completeness2/5

    The tool surface is severely incomplete for web content extraction. It lacks essential operations such as handling different content types (e.g., PDFs, images), managing cookies or authentication, retrying failed requests, or providing metadata extraction. This gap will likely cause agent failures in real-world scenarios beyond basic reading.

  • Average 3.6/5 across 1 of 1 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

  • 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.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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?

    No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: 'fast, token-efficient' (performance characteristics), 'converts to clean Markdown' (output format), and 'preserving links and structure' (content handling). However, it lacks details on error handling, rate limits, authentication needs, or what happens with invalid URLs, which are important for a web scraping tool.

    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 highly concise and well-structured in two sentences. The first sentence establishes the core functionality and ideal use cases, while the second explains the output format and key features. Every word earns its place with no redundancy or unnecessary elaboration.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness3/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool's moderate complexity (web scraping with 3 parameters) and lack of annotations or output schema, the description is partially complete. It covers the purpose, performance, and output format well, but it misses crucial details like parameter semantics, error conditions, and return value structure, which are needed for effective tool invocation.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The schema description coverage is 0%, and the description provides no information about the three parameters (url, pages, cookiesFile). It doesn't explain what 'pages' or 'cookiesFile' mean, their formats, or how they affect the extraction process. This leaves significant gaps in understanding parameter usage beyond what the bare schema provides.

    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's purpose with specific verbs ('extraction', 'reading', 'analyzing', 'gathering') and resources ('web content', 'websites', 'documentation'). It distinguishes itself by emphasizing 'fast, token-efficient' conversion to 'clean Markdown while preserving links and structure', which provides a unique value proposition even without sibling tools.

    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 provides implied usage guidance by listing ideal scenarios ('reading documentation, analyzing content, and gathering information from websites'), but it does not explicitly state when to use this tool versus alternatives or mention any exclusions. With no sibling tools, the lack of comparative guidance is less critical, but it still doesn't offer explicit when/when-not instructions.

    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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